Overview

Brought to you by YData

Dataset statistics

Number of variables47
Number of observations193
Missing cells1670
Missing cells (%)18.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory446.0 KiB
Average record size in memory2.3 KiB

Variable types

Numeric3
Text8
DateTime3
Categorical32
Unsupported1

Alerts

Thrombus has constant value "1" Constant
1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne Fokus is highly overall correlated with Besserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues Defizit and 5 other fieldsHigh correlation
1 = lowgrade 2 = highgrade is highly overall correlated with mibi surgery 0 = neg and 1 other fieldsHigh correlation
ASA is highly overall correlated with Unnamed: 0High correlation
BWS is highly overall correlated with HWS and 3 other fieldsHigh correlation
Besserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues Defizit is highly overall correlated with 1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne Fokus and 8 other fieldsHigh correlation
Fokus abgeklärt is highly overall correlated with add TE and 1 other fieldsHigh correlation
HWS is highly overall correlated with BWS and 3 other fieldsHigh correlation
LWS is highly overall correlated with BWS and 3 other fieldsHigh correlation
Neurologie 1 = Paresen, 2 = vorbestehend, 3 = Tetraparese is highly overall correlated with Besserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues Defizit and 1 other fieldsHigh correlation
RevisionsOP 2 =kein Infekt is highly overall correlated with Unnamed: 0High correlation
Risikofaktoren is highly overall correlated with Neurologie 1 = Paresen, 2 = vorbestehend, 3 = Tetraparese and 4 other fieldsHigh correlation
TE (at all) is highly overall correlated with 1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne Fokus and 2 other fieldsHigh correlation
TE at sus focus 2 = vorOP spinal, 0 Fokus nicht dargestellt, 3 kein Fokus, 4 Fokus weg/saniert, 5 Fokus nicht gefunden is highly overall correlated with other spinal TEHigh correlation
Unnamed: 0 is highly overall correlated with ASA and 8 other fieldsHigh correlation
add TE is highly overall correlated with Fokus abgeklärt and 3 other fieldsHigh correlation
add TE 1 = new focus 2=TE nicht relevant und tatsächlich nicht relevant 3 = nicht untersucht 4 = kein Fokus 5 = bekannter Fokus, schon behandelt 6 Tumor 0 no add TE is highly overall correlated with Unnamed: 0 and 3 other fieldsHigh correlation
ausgeheilt 2=NA 3=dead is highly overall correlated with Unnamed: 0High correlation
biopsy is highly overall correlated with BWS and 3 other fieldsHigh correlation
discitis in MRT = TE, 2 Frage diszitis b MRT unklar, 0 = n übereinstimmend, 3 kein MRT, 4 Ausschluss Diszitis, 5 Diszitis im MRT n erkannt, 6 neuer Nachweis Diszitis is highly overall correlated with 1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne Fokus and 4 other fieldsHigh correlation
histo surgery 3 intermediär 0 neg is highly overall correlated with mibi other and 1 other fieldsHigh correlation
intraspinal is highly overall correlated with BWS and 3 other fieldsHigh correlation
mibi other is highly overall correlated with histo surgery 3 intermediär 0 neg and 1 other fieldsHigh correlation
mibi surgery 0 = neg is highly overall correlated with 1 = lowgrade 2 = highgrade and 3 other fieldsHigh correlation
neuer Fokus nach PET is highly overall correlated with Fokus abgeklärt and 5 other fieldsHigh correlation
nonspinal: overall 2 unklar 3 nicht abklärt 0 nicht übereinstimmend 4 ausgeheilt nach Behandlung 5 nicht abgebildet is highly overall correlated with Unnamed: 0 and 2 other fieldsHigh correlation
other spinal TE is highly overall correlated with 1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne Fokus and 8 other fieldsHigh correlation
reason for PET is highly overall correlated with 1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne Fokus and 5 other fieldsHigh correlation
sex (1F, 2M) is highly overall correlated with weitereHigh correlation
spinal: overall, 0 = nicht übereinstimmend, 1= übereinstimmend, 2 = MRT unklar, 3 kein MRT is highly overall correlated with 1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne Fokus and 4 other fieldsHigh correlation
unspez Fokus abgeklärt 0nein 1ja+neg 2ja+pos is highly overall correlated with Besserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues Defizit and 3 other fieldsHigh correlation
unspez gewertet is highly overall correlated with Besserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues Defizit and 2 other fieldsHigh correlation
weitere is highly overall correlated with sex (1F, 2M) and 1 other fieldsHigh correlation
Fokus abgeklärt is highly imbalanced (51.9%) Imbalance
unspez gewertet is highly imbalanced (81.5%) Imbalance
unspez Fokus abgeklärt 0nein 1ja+neg 2ja+pos is highly imbalanced (78.4%) Imbalance
weitere is highly imbalanced (83.2%) Imbalance
TE (at all) is highly imbalanced (54.9%) Imbalance
other spinal TE is highly imbalanced (50.5%) Imbalance
nonspinal: overall 2 unklar 3 nicht abklärt 0 nicht übereinstimmend 4 ausgeheilt nach Behandlung 5 nicht abgebildet is highly imbalanced (65.3%) Imbalance
reason for PET is highly imbalanced (51.2%) Imbalance
RevisionsOP 2 =kein Infekt is highly imbalanced (51.0%) Imbalance
Neurologie 1 = Paresen, 2 = vorbestehend, 3 = Tetraparese is highly imbalanced (65.5%) Imbalance
Besserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues Defizit is highly imbalanced (72.1%) Imbalance
Thrombus has 178 (92.2%) missing values Missing
DOA has 27 (14.0%) missing values Missing
surgery date has 44 (22.8%) missing values Missing
date of PET has 57 (29.5%) missing values Missing
mibi surgery 0 = neg has 50 (25.9%) missing values Missing
CRP initial has 51 (26.4%) missing values Missing
PETCT TE has 193 (100.0%) missing values Missing
TE (at all) has 51 (26.4%) missing values Missing
add TE has 51 (26.4%) missing values Missing
add TE 1 = new focus 2=TE nicht relevant und tatsächlich nicht relevant 3 = nicht untersucht 4 = kein Fokus 5 = bekannter Fokus, schon behandelt 6 Tumor 0 no add TE has 51 (26.4%) missing values Missing
nonspinal: overall 2 unklar 3 nicht abklärt 0 nicht übereinstimmend 4 ausgeheilt nach Behandlung 5 nicht abgebildet has 51 (26.4%) missing values Missing
RevisionsOP 2 =kein Infekt has 51 (26.4%) missing values Missing
ASA has 51 (26.4%) missing values Missing
ausgeheilt 2=NA 3=dead has 51 (26.4%) missing values Missing
Besserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues Defizit has 165 (85.5%) missing values Missing
(1= DM, 2=iv Drogen, 3=PAVK, 4=Cortisontherapie, 5=Zahnbehandlung, 6=Immunschwäche, 7=zn CTX, 8=Malignom, 9=Infiltrationen, 10=C2, 11=Parkinson, 12 = Niereninsuffizienz) has 185 (95.9%) missing values Missing
Unnamed: 45 has 175 (90.7%) missing values Missing
Unnamed: 46 has 188 (97.4%) missing values Missing
PETCT TE is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-08-30 17:57:00.824601
Analysis finished2025-08-30 17:57:04.538106
Duration3.71 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

High correlation 

Distinct192
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2513152.6
Minimum1025571
Maximum21655353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-08-30T19:57:04.582799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1025571
5-th percentile1250795.6
Q11980797
median2722756
Q32918862
95-th percentile3022092.6
Maximum21655353
Range20629782
Interquartile range (IQR)938065

Descriptive statistics

Standard deviation1516558.9
Coefficient of variation (CV)0.6034488
Kurtosis133.43256
Mean2513152.6
Median Absolute Deviation (MAD)272226
Skewness10.492389
Sum4.8503846 × 108
Variance2.299951 × 1012
MonotonicityNot monotonic
2025-08-30T19:57:04.639045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2774956 2
 
1.0%
1069341 1
 
0.5%
1427060 1
 
0.5%
2801175 1
 
0.5%
2817925 1
 
0.5%
2872465 1
 
0.5%
2876167 1
 
0.5%
2863953 1
 
0.5%
2874820 1
 
0.5%
2872382 1
 
0.5%
Other values (182) 182
94.3%
ValueCountFrequency (%)
1025571 1
0.5%
1050923 1
0.5%
1069341 1
0.5%
1086829 1
0.5%
1110893 1
0.5%
1121310 1
0.5%
1174643 1
0.5%
1187375 1
0.5%
1202379 1
0.5%
1227623 1
0.5%
ValueCountFrequency (%)
21655353 1
0.5%
3052507 1
0.5%
3049199 1
0.5%
3047471 1
0.5%
3041219 1
0.5%
3037105 1
0.5%
3030845 1
0.5%
3030539 1
0.5%
3029294 1
0.5%
3026847 1
0.5%

name
Text

Distinct192
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
2025-08-30T19:57:04.743855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length32
Median length24
Mean length16.207254
Min length9

Characters and Unicode

Total characters3128
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique191 ?
Unique (%)99.0%

Sample

1st rowRankel, Christine
2nd rowMentzel, Frank
3rd rowVial, Renee
4th rowMayr, Daniela
5th rowZellner, Leonhard
ValueCountFrequency (%)
peter 9
 
2.2%
rudolf 6
 
1.5%
fischer 5
 
1.2%
franz 5
 
1.2%
wolfgang 5
 
1.2%
helmut 4
 
1.0%
maria 4
 
1.0%
dr 4
 
1.0%
ingrid 4
 
1.0%
frank 3
 
0.7%
Other values (315) 354
87.8%
2025-08-30T19:57:04.908465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 340
 
10.9%
r 279
 
8.9%
a 224
 
7.2%
210
 
6.7%
, 193
 
6.2%
i 192
 
6.1%
n 169
 
5.4%
l 163
 
5.2%
t 111
 
3.5%
h 101
 
3.2%
Other values (48) 1146
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3128
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 340
 
10.9%
r 279
 
8.9%
a 224
 
7.2%
210
 
6.7%
, 193
 
6.2%
i 192
 
6.1%
n 169
 
5.4%
l 163
 
5.2%
t 111
 
3.5%
h 101
 
3.2%
Other values (48) 1146
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3128
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 340
 
10.9%
r 279
 
8.9%
a 224
 
7.2%
210
 
6.7%
, 193
 
6.2%
i 192
 
6.1%
n 169
 
5.4%
l 163
 
5.2%
t 111
 
3.5%
h 101
 
3.2%
Other values (48) 1146
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3128
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 340
 
10.9%
r 279
 
8.9%
a 224
 
7.2%
210
 
6.7%
, 193
 
6.2%
i 192
 
6.1%
n 169
 
5.4%
l 163
 
5.2%
t 111
 
3.5%
h 101
 
3.2%
Other values (48) 1146
36.6%

DOB
Date

Distinct192
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Minimum1930-12-05 00:00:00
Maximum1994-11-11 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-30T19:57:04.975828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-30T19:57:05.032010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

age
Real number (ℝ)

Distinct54
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.994819
Minimum24
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-08-30T19:57:05.086916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile40.6
Q161
median71
Q380
95-th percentile85
Maximum92
Range68
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.347713
Coefficient of variation (CV)0.19345964
Kurtosis0.72718467
Mean68.994819
Median Absolute Deviation (MAD)9
Skewness-0.92680286
Sum13316
Variance178.16143
MonotonicityNot monotonic
2025-08-30T19:57:05.138047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 9
 
4.7%
81 9
 
4.7%
85 9
 
4.7%
75 8
 
4.1%
82 8
 
4.1%
78 7
 
3.6%
69 7
 
3.6%
71 7
 
3.6%
77 7
 
3.6%
74 6
 
3.1%
Other values (44) 116
60.1%
ValueCountFrequency (%)
24 1
0.5%
27 1
0.5%
32 1
0.5%
34 1
0.5%
36 1
0.5%
37 1
0.5%
38 1
0.5%
39 2
1.0%
40 1
0.5%
41 1
0.5%
ValueCountFrequency (%)
92 1
 
0.5%
91 1
 
0.5%
88 2
 
1.0%
87 2
 
1.0%
86 2
 
1.0%
85 9
4.7%
84 6
3.1%
83 3
 
1.6%
82 8
4.1%
81 9
4.7%

Fokus abgeklärt
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
-
134 
1
51 
0
 
5
2
 
2
None
 
1

Length

Max length4
Median length1
Mean length1.015544
Min length1

Characters and Unicode

Total characters196
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 134
69.4%
1 51
 
26.4%
0 5
 
2.6%
2 2
 
1.0%
None 1
 
0.5%

Length

2025-08-30T19:57:05.185953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:05.226601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
134
69.4%
1 51
 
26.4%
0 5
 
2.6%
2 2
 
1.0%
none 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
- 134
68.4%
1 51
 
26.0%
0 5
 
2.6%
2 2
 
1.0%
N 1
 
0.5%
o 1
 
0.5%
n 1
 
0.5%
e 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 134
68.4%
1 51
 
26.0%
0 5
 
2.6%
2 2
 
1.0%
N 1
 
0.5%
o 1
 
0.5%
n 1
 
0.5%
e 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 134
68.4%
1 51
 
26.0%
0 5
 
2.6%
2 2
 
1.0%
N 1
 
0.5%
o 1
 
0.5%
n 1
 
0.5%
e 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 134
68.4%
1 51
 
26.0%
0 5
 
2.6%
2 2
 
1.0%
N 1
 
0.5%
o 1
 
0.5%
n 1
 
0.5%
e 1
 
0.5%

unspez gewertet
Categorical

High correlation  Imbalance 

Distinct14
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
-
177 
Colitis
 
4
HSM
 
1
kleine Gelenke, ae rheumatologisch
 
1
Pneumonie
 
1
Other values (9)
 
9

Length

Max length34
Median length1
Mean length1.7253886
Min length1

Characters and Unicode

Total characters333
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)6.2%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 177
91.7%
Colitis 4
 
2.1%
HSM 1
 
0.5%
kleine Gelenke, ae rheumatologisch 1
 
0.5%
Pneumonie 1
 
0.5%
AC Gelenk 1
 
0.5%
Magen 1
 
0.5%
Sigmoiditis 1
 
0.5%
Schulter 1
 
0.5%
Dekubitus Knöchel 1
 
0.5%
Other values (4) 4
 
2.1%

Length

2025-08-30T19:57:05.274554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
177
89.4%
colitis 4
 
2.0%
magen 1
 
0.5%
none 1
 
0.5%
schulter/becken 1
 
0.5%
hüft-tep 1
 
0.5%
knöchel 1
 
0.5%
dekubitus 1
 
0.5%
schulter 1
 
0.5%
sigmoiditis 1
 
0.5%
Other values (9) 9
 
4.5%

Most occurring characters

ValueCountFrequency (%)
- 178
53.5%
e 19
 
5.7%
i 16
 
4.8%
l 12
 
3.6%
o 11
 
3.3%
n 10
 
3.0%
t 10
 
3.0%
s 7
 
2.1%
u 6
 
1.8%
C 6
 
1.8%
Other values (27) 58
 
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 333
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 178
53.5%
e 19
 
5.7%
i 16
 
4.8%
l 12
 
3.6%
o 11
 
3.3%
n 10
 
3.0%
t 10
 
3.0%
s 7
 
2.1%
u 6
 
1.8%
C 6
 
1.8%
Other values (27) 58
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 333
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 178
53.5%
e 19
 
5.7%
i 16
 
4.8%
l 12
 
3.6%
o 11
 
3.3%
n 10
 
3.0%
t 10
 
3.0%
s 7
 
2.1%
u 6
 
1.8%
C 6
 
1.8%
Other values (27) 58
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 333
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 178
53.5%
e 19
 
5.7%
i 16
 
4.8%
l 12
 
3.6%
o 11
 
3.3%
n 10
 
3.0%
t 10
 
3.0%
s 7
 
2.1%
u 6
 
1.8%
C 6
 
1.8%
Other values (27) 58
 
17.4%

unspez Fokus abgeklärt 0nein 1ja+neg 2ja+pos
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
-
177 
1
 
8
0
 
5
2
 
1
Divertikel
 
1

Length

Max length10
Median length1
Mean length1.0621762
Min length1

Characters and Unicode

Total characters205
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.6%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 177
91.7%
1 8
 
4.1%
0 5
 
2.6%
2 1
 
0.5%
Divertikel 1
 
0.5%
None 1
 
0.5%

Length

2025-08-30T19:57:05.385894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:05.425271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
177
91.7%
1 8
 
4.1%
0 5
 
2.6%
2 1
 
0.5%
divertikel 1
 
0.5%
none 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
- 177
86.3%
1 8
 
3.9%
0 5
 
2.4%
e 3
 
1.5%
i 2
 
1.0%
2 1
 
0.5%
D 1
 
0.5%
v 1
 
0.5%
r 1
 
0.5%
t 1
 
0.5%
Other values (5) 5
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 177
86.3%
1 8
 
3.9%
0 5
 
2.4%
e 3
 
1.5%
i 2
 
1.0%
2 1
 
0.5%
D 1
 
0.5%
v 1
 
0.5%
r 1
 
0.5%
t 1
 
0.5%
Other values (5) 5
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 177
86.3%
1 8
 
3.9%
0 5
 
2.4%
e 3
 
1.5%
i 2
 
1.0%
2 1
 
0.5%
D 1
 
0.5%
v 1
 
0.5%
r 1
 
0.5%
t 1
 
0.5%
Other values (5) 5
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 177
86.3%
1 8
 
3.9%
0 5
 
2.4%
e 3
 
1.5%
i 2
 
1.0%
2 1
 
0.5%
D 1
 
0.5%
v 1
 
0.5%
r 1
 
0.5%
t 1
 
0.5%
Other values (5) 5
 
2.4%

weitere
Categorical

High correlation  Imbalance 

Distinct14
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
-
179 
None
 
2
Ulcus, bekannt
 
1
BronchialCA
 
1
Zn Pankreatitis
 
1
Other values (9)
 
9

Length

Max length41
Median length1
Mean length1.8134715
Min length1

Characters and Unicode

Total characters350
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)6.2%

Sample

1st row-
2nd rowUlcus, bekannt
3rd rowBronchialCA
4th rowZn Pankreatitis
5th row-

Common Values

ValueCountFrequency (%)
- 179
92.7%
None 2
 
1.0%
Ulcus, bekannt 1
 
0.5%
BronchialCA 1
 
0.5%
Zn Pankreatitis 1
 
0.5%
Neurinom 1
 
0.5%
Va ColonCA 1
 
0.5%
Mtx 1
 
0.5%
lymphogen pleural pulmonal metastasiertem 1
 
0.5%
Struma 1
 
0.5%
Other values (4) 4
 
2.1%

Length

2025-08-30T19:57:05.476117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
179
88.2%
adenom 2
 
1.0%
none 2
 
1.0%
pleural 1
 
0.5%
prostataca 1
 
0.5%
ed 1
 
0.5%
leberzirrhose 1
 
0.5%
ae 1
 
0.5%
nnr-rf 1
 
0.5%
struma 1
 
0.5%
Other values (13) 13
 
6.4%

Most occurring characters

ValueCountFrequency (%)
- 180
51.4%
e 16
 
4.6%
o 14
 
4.0%
n 14
 
4.0%
a 13
 
3.7%
10
 
2.9%
r 10
 
2.9%
t 10
 
2.9%
l 9
 
2.6%
m 8
 
2.3%
Other values (29) 66
 
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 180
51.4%
e 16
 
4.6%
o 14
 
4.0%
n 14
 
4.0%
a 13
 
3.7%
10
 
2.9%
r 10
 
2.9%
t 10
 
2.9%
l 9
 
2.6%
m 8
 
2.3%
Other values (29) 66
 
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 180
51.4%
e 16
 
4.6%
o 14
 
4.0%
n 14
 
4.0%
a 13
 
3.7%
10
 
2.9%
r 10
 
2.9%
t 10
 
2.9%
l 9
 
2.6%
m 8
 
2.3%
Other values (29) 66
 
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 180
51.4%
e 16
 
4.6%
o 14
 
4.0%
n 14
 
4.0%
a 13
 
3.7%
10
 
2.9%
r 10
 
2.9%
t 10
 
2.9%
l 9
 
2.6%
m 8
 
2.3%
Other values (29) 66
 
18.9%

Thrombus
Categorical

Constant  Missing 

Distinct1
Distinct (%)6.7%
Missing178
Missing (%)92.2%
Memory size16.0 KiB
1
15 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 15
 
7.8%
(Missing) 178
92.2%

Length

2025-08-30T19:57:05.521908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:05.556891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 15
100.0%

Most occurring characters

ValueCountFrequency (%)
1 15
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 15
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 15
100.0%

sex (1F, 2M)
Categorical

High correlation 

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
2
127 
1
65 
None
 
1

Length

Max length4
Median length1
Mean length1.015544
Min length1

Characters and Unicode

Total characters196
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 127
65.8%
1 65
33.7%
None 1
 
0.5%

Length

2025-08-30T19:57:05.617851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:05.656264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 127
65.8%
1 65
33.7%
none 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
2 127
64.8%
1 65
33.2%
N 1
 
0.5%
o 1
 
0.5%
n 1
 
0.5%
e 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 127
64.8%
1 65
33.2%
N 1
 
0.5%
o 1
 
0.5%
n 1
 
0.5%
e 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 127
64.8%
1 65
33.2%
N 1
 
0.5%
o 1
 
0.5%
n 1
 
0.5%
e 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 127
64.8%
1 65
33.2%
N 1
 
0.5%
o 1
 
0.5%
n 1
 
0.5%
e 1
 
0.5%

Diagnose
Text

Distinct181
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
2025-08-30T19:57:05.739138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length190
Median length79
Mean length41.813472
Min length4

Characters and Unicode

Total characters8070
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique173 ?
Unique (%)89.6%

Sample

1st rowSpondylodiszitis TH5/6
2nd rowDiszitis L4/5
3rd rowSpondylodiszitis L4/5/S1+Empyem
4th rowAusschluss Diszitis
5th rowWHST
ValueCountFrequency (%)
spondylodiszitis 113
 
10.8%
z.n 33
 
3.2%
bei 30
 
2.9%
zn 29
 
2.8%
28
 
2.7%
l4/5 26
 
2.5%
diszitis 26
 
2.5%
empyem 25
 
2.4%
mit 24
 
2.3%
b 21
 
2.0%
Other values (338) 689
66.0%
2025-08-30T19:57:05.901157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
854
 
10.6%
i 645
 
8.0%
s 557
 
6.9%
o 396
 
4.9%
n 383
 
4.7%
e 350
 
4.3%
d 325
 
4.0%
t 292
 
3.6%
l 249
 
3.1%
S 233
 
2.9%
Other values (61) 3786
46.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
854
 
10.6%
i 645
 
8.0%
s 557
 
6.9%
o 396
 
4.9%
n 383
 
4.7%
e 350
 
4.3%
d 325
 
4.0%
t 292
 
3.6%
l 249
 
3.1%
S 233
 
2.9%
Other values (61) 3786
46.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
854
 
10.6%
i 645
 
8.0%
s 557
 
6.9%
o 396
 
4.9%
n 383
 
4.7%
e 350
 
4.3%
d 325
 
4.0%
t 292
 
3.6%
l 249
 
3.1%
S 233
 
2.9%
Other values (61) 3786
46.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
854
 
10.6%
i 645
 
8.0%
s 557
 
6.9%
o 396
 
4.9%
n 383
 
4.7%
e 350
 
4.3%
d 325
 
4.0%
t 292
 
3.6%
l 249
 
3.1%
S 233
 
2.9%
Other values (61) 3786
46.9%

LWS
Categorical

High correlation 

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
1
125 
0
54 
None
14 

Length

Max length4
Median length1
Mean length1.2176166
Min length1

Characters and Unicode

Total characters235
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 125
64.8%
0 54
28.0%
None 14
 
7.3%

Length

2025-08-30T19:57:05.964126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:06.003353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 125
64.8%
0 54
28.0%
none 14
 
7.3%

Most occurring characters

ValueCountFrequency (%)
1 125
53.2%
0 54
23.0%
N 14
 
6.0%
o 14
 
6.0%
n 14
 
6.0%
e 14
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 235
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 125
53.2%
0 54
23.0%
N 14
 
6.0%
o 14
 
6.0%
n 14
 
6.0%
e 14
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 235
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 125
53.2%
0 54
23.0%
N 14
 
6.0%
o 14
 
6.0%
n 14
 
6.0%
e 14
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 235
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 125
53.2%
0 54
23.0%
N 14
 
6.0%
o 14
 
6.0%
n 14
 
6.0%
e 14
 
6.0%

BWS
Categorical

High correlation 

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
0
128 
1
52 
None
13 

Length

Max length4
Median length1
Mean length1.2020725
Min length1

Characters and Unicode

Total characters232
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 128
66.3%
1 52
26.9%
None 13
 
6.7%

Length

2025-08-30T19:57:06.045860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:06.085443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 128
66.3%
1 52
26.9%
none 13
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 128
55.2%
1 52
22.4%
N 13
 
5.6%
o 13
 
5.6%
n 13
 
5.6%
e 13
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 128
55.2%
1 52
22.4%
N 13
 
5.6%
o 13
 
5.6%
n 13
 
5.6%
e 13
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 128
55.2%
1 52
22.4%
N 13
 
5.6%
o 13
 
5.6%
n 13
 
5.6%
e 13
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 128
55.2%
1 52
22.4%
N 13
 
5.6%
o 13
 
5.6%
n 13
 
5.6%
e 13
 
5.6%

HWS
Categorical

High correlation 

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
0
148 
1
32 
None
 
13

Length

Max length4
Median length1
Mean length1.2020725
Min length1

Characters and Unicode

Total characters232
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 148
76.7%
1 32
 
16.6%
None 13
 
6.7%

Length

2025-08-30T19:57:06.127981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:06.167165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 148
76.7%
1 32
 
16.6%
none 13
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 148
63.8%
1 32
 
13.8%
N 13
 
5.6%
o 13
 
5.6%
n 13
 
5.6%
e 13
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 148
63.8%
1 32
 
13.8%
N 13
 
5.6%
o 13
 
5.6%
n 13
 
5.6%
e 13
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 148
63.8%
1 32
 
13.8%
N 13
 
5.6%
o 13
 
5.6%
n 13
 
5.6%
e 13
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 148
63.8%
1 32
 
13.8%
N 13
 
5.6%
o 13
 
5.6%
n 13
 
5.6%
e 13
 
5.6%

intraspinal
Categorical

High correlation 

Distinct4
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
0
129 
1
48 
None
13 
2
 
3

Length

Max length4
Median length1
Mean length1.2020725
Min length1

Characters and Unicode

Total characters232
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 129
66.8%
1 48
 
24.9%
None 13
 
6.7%
2 3
 
1.6%

Length

2025-08-30T19:57:06.209632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:06.250097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 129
66.8%
1 48
 
24.9%
none 13
 
6.7%
2 3
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 129
55.6%
1 48
 
20.7%
N 13
 
5.6%
o 13
 
5.6%
n 13
 
5.6%
e 13
 
5.6%
2 3
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 129
55.6%
1 48
 
20.7%
N 13
 
5.6%
o 13
 
5.6%
n 13
 
5.6%
e 13
 
5.6%
2 3
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 129
55.6%
1 48
 
20.7%
N 13
 
5.6%
o 13
 
5.6%
n 13
 
5.6%
e 13
 
5.6%
2 3
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 129
55.6%
1 48
 
20.7%
N 13
 
5.6%
o 13
 
5.6%
n 13
 
5.6%
e 13
 
5.6%
2 3
 
1.3%

biopsy
Categorical

High correlation 

Distinct5
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
0
122 
1
55 
None
14 
1 (LWS)
 
1
2
 
1

Length

Max length7
Median length1
Mean length1.2487047
Min length1

Characters and Unicode

Total characters241
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 122
63.2%
1 55
28.5%
None 14
 
7.3%
1 (LWS) 1
 
0.5%
2 1
 
0.5%

Length

2025-08-30T19:57:06.293208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:06.334447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 122
62.9%
1 56
28.9%
none 14
 
7.2%
lws 1
 
0.5%
2 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 122
50.6%
1 56
23.2%
N 14
 
5.8%
o 14
 
5.8%
n 14
 
5.8%
e 14
 
5.8%
1
 
0.4%
( 1
 
0.4%
L 1
 
0.4%
W 1
 
0.4%
Other values (3) 3
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 241
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 122
50.6%
1 56
23.2%
N 14
 
5.8%
o 14
 
5.8%
n 14
 
5.8%
e 14
 
5.8%
1
 
0.4%
( 1
 
0.4%
L 1
 
0.4%
W 1
 
0.4%
Other values (3) 3
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 241
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 122
50.6%
1 56
23.2%
N 14
 
5.8%
o 14
 
5.8%
n 14
 
5.8%
e 14
 
5.8%
1
 
0.4%
( 1
 
0.4%
L 1
 
0.4%
W 1
 
0.4%
Other values (3) 3
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 241
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 122
50.6%
1 56
23.2%
N 14
 
5.8%
o 14
 
5.8%
n 14
 
5.8%
e 14
 
5.8%
1
 
0.4%
( 1
 
0.4%
L 1
 
0.4%
W 1
 
0.4%
Other values (3) 3
 
1.2%

OP
Text

Distinct155
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
2025-08-30T19:57:06.412302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length344
Median length81
Mean length34.787565
Min length1

Characters and Unicode

Total characters6714
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)76.7%

Sample

1st rowSolera TH4-5-6-7 + Deko+BSF-Ausräumung
2nd rowSolera LW2-3-S2-IA, ALIF L4/5
3rd rowSolera L4-5-S1+Deko, ALIF L4/5, 5/S1
4th row-
5th rowVerlängerung L2-3-4-5-S1-S2IA
ValueCountFrequency (%)
solera 94
 
9.9%
53
 
5.6%
xlif 26
 
2.7%
wke 20
 
2.1%
none 16
 
1.7%
deko 16
 
1.7%
l4/5 16
 
1.7%
l5/s1 15
 
1.6%
acdf 15
 
1.6%
oss 13
 
1.4%
Other values (375) 668
70.2%
2025-08-30T19:57:06.569657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
760
 
11.3%
e 459
 
6.8%
- 345
 
5.1%
o 272
 
4.1%
L 257
 
3.8%
n 247
 
3.7%
r 228
 
3.4%
a 225
 
3.4%
S 218
 
3.2%
1 200
 
3.0%
Other values (58) 3503
52.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6714
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
760
 
11.3%
e 459
 
6.8%
- 345
 
5.1%
o 272
 
4.1%
L 257
 
3.8%
n 247
 
3.7%
r 228
 
3.4%
a 225
 
3.4%
S 218
 
3.2%
1 200
 
3.0%
Other values (58) 3503
52.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6714
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
760
 
11.3%
e 459
 
6.8%
- 345
 
5.1%
o 272
 
4.1%
L 257
 
3.8%
n 247
 
3.7%
r 228
 
3.4%
a 225
 
3.4%
S 218
 
3.2%
1 200
 
3.0%
Other values (58) 3503
52.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6714
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
760
 
11.3%
e 459
 
6.8%
- 345
 
5.1%
o 272
 
4.1%
L 257
 
3.8%
n 247
 
3.7%
r 228
 
3.4%
a 225
 
3.4%
S 218
 
3.2%
1 200
 
3.0%
Other values (58) 3503
52.2%

DOA
Date

Missing 

Distinct158
Distinct (%)95.2%
Missing27
Missing (%)14.0%
Memory size7.1 KiB
Minimum2016-03-21 00:00:00
Maximum2023-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-30T19:57:06.637209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-30T19:57:06.692685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

surgery date
Text

Missing 

Distinct134
Distinct (%)89.9%
Missing44
Missing (%)22.8%
Memory size15.4 KiB
2025-08-30T19:57:06.783565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length21
Median length9
Mean length8.7449664
Min length1

Characters and Unicode

Total characters1303
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)84.6%

Sample

1st row30/5/2022
2nd row3/3/2022
3rd row22/12/2022
4th row-
5th row1/8/2023
ValueCountFrequency (%)
8
 
5.3%
24/10/2023 3
 
2.0%
7/12/2022 2
 
1.3%
24/9/2021 2
 
1.3%
06.05.2020 2
 
1.3%
17.03.2020 2
 
1.3%
28/4/2023 2
 
1.3%
28/7/2023 2
 
1.3%
21/9/2023 1
 
0.7%
14/8/2023 1
 
0.7%
Other values (125) 125
83.3%
2025-08-30T19:57:06.942454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 398
30.5%
/ 240
18.4%
0 220
16.9%
1 144
 
11.1%
3 72
 
5.5%
. 44
 
3.4%
6 34
 
2.6%
8 33
 
2.5%
4 28
 
2.1%
9 28
 
2.1%
Other values (4) 62
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1303
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 398
30.5%
/ 240
18.4%
0 220
16.9%
1 144
 
11.1%
3 72
 
5.5%
. 44
 
3.4%
6 34
 
2.6%
8 33
 
2.5%
4 28
 
2.1%
9 28
 
2.1%
Other values (4) 62
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1303
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 398
30.5%
/ 240
18.4%
0 220
16.9%
1 144
 
11.1%
3 72
 
5.5%
. 44
 
3.4%
6 34
 
2.6%
8 33
 
2.5%
4 28
 
2.1%
9 28
 
2.1%
Other values (4) 62
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1303
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 398
30.5%
/ 240
18.4%
0 220
16.9%
1 144
 
11.1%
3 72
 
5.5%
. 44
 
3.4%
6 34
 
2.6%
8 33
 
2.5%
4 28
 
2.1%
9 28
 
2.1%
Other values (4) 62
 
4.8%

date of PET
Date

Missing 

Distinct124
Distinct (%)91.2%
Missing57
Missing (%)29.5%
Memory size7.1 KiB
Minimum2016-03-23 00:00:00
Maximum2024-02-20 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-30T19:57:07.013572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-30T19:57:07.069691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

mibi surgery 0 = neg
Categorical

High correlation  Missing 

Distinct40
Distinct (%)28.0%
Missing50
Missing (%)25.9%
Memory size17.4 KiB
0
36 
STAU
26 
not done
16 
Cutibacterium (Propionibacterium) acnes
11 
Staphylococcus epidermidis
10 
Other values (35)
44 

Length

Max length94
Median length71
Mean length16
Min length1

Characters and Unicode

Total characters2288
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)19.6%

Sample

1st rowStreptococcus dysgalactiae, Staphylococcus capitis
2nd rowStaphylococcus epidermidis, Staphylococcus warneri
3rd rowStaphylococcus epidermidis
4th rownot done
5th rowStaphylococcus epidermidis

Common Values

ValueCountFrequency (%)
0 36
18.7%
STAU 26
13.5%
not done 16
 
8.3%
Cutibacterium (Propionibacterium) acnes 11
 
5.7%
Staphylococcus epidermidis 10
 
5.2%
E. coli 4
 
2.1%
Streptococcus intermedius 2
 
1.0%
E. faecalis 2
 
1.0%
Pseudomonas aeruginosa 2
 
1.0%
MRSA 2
 
1.0%
Other values (30) 32
16.6%
(Missing) 50
25.9%

Length

2025-08-30T19:57:07.122240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 36
12.9%
stau 31
11.2%
staphylococcus 27
 
9.7%
cutibacterium 20
 
7.2%
acnes 19
 
6.8%
not 16
 
5.8%
done 16
 
5.8%
propionibacterium 16
 
5.8%
epidermidis 14
 
5.0%
e 7
 
2.5%
Other values (44) 76
27.3%

Most occurring characters

ValueCountFrequency (%)
c 199
 
8.7%
i 181
 
7.9%
o 170
 
7.4%
e 160
 
7.0%
a 159
 
6.9%
t 139
 
6.1%
136
 
5.9%
u 113
 
4.9%
r 107
 
4.7%
s 105
 
4.6%
Other values (33) 819
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 199
 
8.7%
i 181
 
7.9%
o 170
 
7.4%
e 160
 
7.0%
a 159
 
6.9%
t 139
 
6.1%
136
 
5.9%
u 113
 
4.9%
r 107
 
4.7%
s 105
 
4.6%
Other values (33) 819
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 199
 
8.7%
i 181
 
7.9%
o 170
 
7.4%
e 160
 
7.0%
a 159
 
6.9%
t 139
 
6.1%
136
 
5.9%
u 113
 
4.9%
r 107
 
4.7%
s 105
 
4.6%
Other values (33) 819
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 199
 
8.7%
i 181
 
7.9%
o 170
 
7.4%
e 160
 
7.0%
a 159
 
6.9%
t 139
 
6.1%
136
 
5.9%
u 113
 
4.9%
r 107
 
4.7%
s 105
 
4.6%
Other values (33) 819
35.8%

1 = lowgrade 2 = highgrade
Categorical

High correlation 

Distinct5
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
None
92 
2
60 
1
27 
0
13 
xx
 
1

Length

Max length4
Median length1
Mean length2.4352332
Min length1

Characters and Unicode

Total characters470
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row2
2nd row1
3rd row1
4th rowNone
5th row1

Common Values

ValueCountFrequency (%)
None 92
47.7%
2 60
31.1%
1 27
 
14.0%
0 13
 
6.7%
xx 1
 
0.5%

Length

2025-08-30T19:57:07.166922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:07.208548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
none 92
47.7%
2 60
31.1%
1 27
 
14.0%
0 13
 
6.7%
xx 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
N 92
19.6%
o 92
19.6%
n 92
19.6%
e 92
19.6%
2 60
12.8%
1 27
 
5.7%
0 13
 
2.8%
x 2
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 92
19.6%
o 92
19.6%
n 92
19.6%
e 92
19.6%
2 60
12.8%
1 27
 
5.7%
0 13
 
2.8%
x 2
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 92
19.6%
o 92
19.6%
n 92
19.6%
e 92
19.6%
2 60
12.8%
1 27
 
5.7%
0 13
 
2.8%
x 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 92
19.6%
o 92
19.6%
n 92
19.6%
e 92
19.6%
2 60
12.8%
1 27
 
5.7%
0 13
 
2.8%
x 2
 
0.4%

histo surgery 3 intermediär 0 neg
Categorical

High correlation 

Distinct8
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
1
96 
None
51 
not done
25 
0
11 
2
 
5
Other values (3)
 
5

Length

Max length16
Median length1
Mean length2.7772021
Min length1

Characters and Unicode

Total characters536
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row1
2nd row1
3rd row1
4th rownot done
5th row1

Common Values

ValueCountFrequency (%)
1 96
49.7%
None 51
26.4%
not done 25
 
13.0%
0 11
 
5.7%
2 5
 
2.6%
3 3
 
1.6%
- 1
 
0.5%
1 B-Zell Lymphom 1
 
0.5%

Length

2025-08-30T19:57:07.255517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:07.301424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 97
44.1%
none 51
23.2%
not 25
 
11.4%
done 25
 
11.4%
0 11
 
5.0%
2 5
 
2.3%
3 3
 
1.4%
1
 
0.5%
b-zell 1
 
0.5%
lymphom 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 102
19.0%
n 101
18.8%
1 97
18.1%
e 77
14.4%
N 51
9.5%
27
 
5.0%
t 25
 
4.7%
d 25
 
4.7%
0 11
 
2.1%
2 5
 
0.9%
Other values (10) 15
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 102
19.0%
n 101
18.8%
1 97
18.1%
e 77
14.4%
N 51
9.5%
27
 
5.0%
t 25
 
4.7%
d 25
 
4.7%
0 11
 
2.1%
2 5
 
0.9%
Other values (10) 15
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 102
19.0%
n 101
18.8%
1 97
18.1%
e 77
14.4%
N 51
9.5%
27
 
5.0%
t 25
 
4.7%
d 25
 
4.7%
0 11
 
2.1%
2 5
 
0.9%
Other values (10) 15
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 102
19.0%
n 101
18.8%
1 97
18.1%
e 77
14.4%
N 51
9.5%
27
 
5.0%
t 25
 
4.7%
d 25
 
4.7%
0 11
 
2.1%
2 5
 
0.9%
Other values (10) 15
 
2.8%

mibi other
Categorical

High correlation 

Distinct42
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
-
67 
None
56 
STAU (BK)
22 
0
 
6
E. coli (BK)
 
3
Other values (37)
39 

Length

Max length77
Median length76
Mean length8.5388601
Min length1

Characters and Unicode

Total characters1648
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)18.1%

Sample

1st rowNone
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 67
34.7%
None 56
29.0%
STAU (BK) 22
 
11.4%
0 6
 
3.1%
E. coli (BK) 3
 
1.6%
MRSA (BK) 2
 
1.0%
Staphylococcus agalacticae (BK, Knie) 2
 
1.0%
S hominis (BK) 1
 
0.5%
Staphylococcus epidermidis, Cutibacterium (Propionibacterium) avidum (Aorta) 1
 
0.5%
STAU, Streptococcus agalactiae (BK) 1
 
0.5%
Other values (32) 32
16.6%

Length

2025-08-30T19:57:07.358788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
67
21.7%
none 56
18.1%
bk 49
15.9%
stau 32
10.4%
staphylococcus 10
 
3.2%
0 6
 
1.9%
e 5
 
1.6%
epidermidis 5
 
1.6%
knie 4
 
1.3%
streptococcus 4
 
1.3%
Other values (50) 71
23.0%

Most occurring characters

ValueCountFrequency (%)
e 136
 
8.3%
o 121
 
7.3%
116
 
7.0%
n 88
 
5.3%
c 84
 
5.1%
a 71
 
4.3%
- 68
 
4.1%
i 67
 
4.1%
( 63
 
3.8%
) 63
 
3.8%
Other values (39) 771
46.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 136
 
8.3%
o 121
 
7.3%
116
 
7.0%
n 88
 
5.3%
c 84
 
5.1%
a 71
 
4.3%
- 68
 
4.1%
i 67
 
4.1%
( 63
 
3.8%
) 63
 
3.8%
Other values (39) 771
46.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 136
 
8.3%
o 121
 
7.3%
116
 
7.0%
n 88
 
5.3%
c 84
 
5.1%
a 71
 
4.3%
- 68
 
4.1%
i 67
 
4.1%
( 63
 
3.8%
) 63
 
3.8%
Other values (39) 771
46.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 136
 
8.3%
o 121
 
7.3%
116
 
7.0%
n 88
 
5.3%
c 84
 
5.1%
a 71
 
4.3%
- 68
 
4.1%
i 67
 
4.1%
( 63
 
3.8%
) 63
 
3.8%
Other values (39) 771
46.8%

CRP initial
Real number (ℝ)

Missing 

Distinct107
Distinct (%)75.4%
Missing51
Missing (%)26.4%
Infinite0
Infinite (%)0.0%
Mean10.564789
Minimum0.1
Maximum46.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-08-30T19:57:07.409843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.305
Q12.975
median8.2
Q316.575
95-th percentile29.8
Maximum46.2
Range46.1
Interquartile range (IQR)13.6

Descriptive statistics

Standard deviation9.5979992
Coefficient of variation (CV)0.90848946
Kurtosis1.1572038
Mean10.564789
Median Absolute Deviation (MAD)6
Skewness1.1934527
Sum1500.2
Variance92.121588
MonotonicityNot monotonic
2025-08-30T19:57:07.531030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 5
 
2.6%
4.6 4
 
2.1%
2.2 3
 
1.6%
8.9 2
 
1.0%
7 2
 
1.0%
5.1 2
 
1.0%
9.1 2
 
1.0%
0.5 2
 
1.0%
1.4 2
 
1.0%
6.6 2
 
1.0%
Other values (97) 116
60.1%
(Missing) 51
26.4%
ValueCountFrequency (%)
0.1 2
 
1.0%
0.2 1
 
0.5%
0.3 5
2.6%
0.4 1
 
0.5%
0.5 2
 
1.0%
0.6 1
 
0.5%
0.7 2
 
1.0%
0.8 2
 
1.0%
0.9 1
 
0.5%
1 2
 
1.0%
ValueCountFrequency (%)
46.2 1
0.5%
41.4 1
0.5%
34.7 1
0.5%
33.3 1
0.5%
32.7 1
0.5%
31.9 1
0.5%
29.9 1
0.5%
29.8 2
1.0%
29.5 1
0.5%
26.1 1
0.5%

PETCT TE
Unsupported

Missing  Rejected  Unsupported 

Missing193
Missing (%)100.0%
Memory size7.1 KiB

TE (at all)
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)2.1%
Missing51
Missing (%)26.4%
Memory size15.3 KiB
1
117 
0
24 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters142
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 117
60.6%
0 24
 
12.4%
2 1
 
0.5%
(Missing) 51
26.4%

Length

2025-08-30T19:57:07.577151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:07.614155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 117
82.4%
0 24
 
16.9%
2 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 117
82.4%
0 24
 
16.9%
2 1
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 117
82.4%
0 24
 
16.9%
2 1
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 117
82.4%
0 24
 
16.9%
2 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 117
82.4%
0 24
 
16.9%
2 1
 
0.7%

discitis in MRT = TE, 2 Frage diszitis b MRT unklar, 0 = n übereinstimmend, 3 kein MRT, 4 Ausschluss Diszitis, 5 Diszitis im MRT n erkannt, 6 neuer Nachweis Diszitis
Categorical

High correlation 

Distinct11
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
1
92 
None
51 
2
23 
0
 
9
3
 
9
Other values (6)
 
9

Length

Max length4
Median length1
Mean length1.8497409
Min length1

Characters and Unicode

Total characters357
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)2.1%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 92
47.7%
None 51
26.4%
2 23
 
11.9%
0 9
 
4.7%
3 9
 
4.7%
4 3
 
1.6%
2, 4 2
 
1.0%
5 1
 
0.5%
3, 4 1
 
0.5%
6 1
 
0.5%

Length

2025-08-30T19:57:07.657179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 92
46.9%
none 51
26.0%
2 25
 
12.8%
3 10
 
5.1%
0 9
 
4.6%
4 6
 
3.1%
5 1
 
0.5%
6 1
 
0.5%
0/1 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 93
26.1%
N 51
14.3%
o 51
14.3%
n 51
14.3%
e 51
14.3%
2 25
 
7.0%
0 10
 
2.8%
3 10
 
2.8%
4 6
 
1.7%
, 3
 
0.8%
Other values (4) 6
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 357
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 93
26.1%
N 51
14.3%
o 51
14.3%
n 51
14.3%
e 51
14.3%
2 25
 
7.0%
0 10
 
2.8%
3 10
 
2.8%
4 6
 
1.7%
, 3
 
0.8%
Other values (4) 6
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 357
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 93
26.1%
N 51
14.3%
o 51
14.3%
n 51
14.3%
e 51
14.3%
2 25
 
7.0%
0 10
 
2.8%
3 10
 
2.8%
4 6
 
1.7%
, 3
 
0.8%
Other values (4) 6
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 357
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 93
26.1%
N 51
14.3%
o 51
14.3%
n 51
14.3%
e 51
14.3%
2 25
 
7.0%
0 10
 
2.8%
3 10
 
2.8%
4 6
 
1.7%
, 3
 
0.8%
Other values (4) 6
 
1.7%

other spinal TE
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
0
138 
None
51 
3
 
2
1
 
2

Length

Max length4
Median length1
Mean length1.7927461
Min length1

Characters and Unicode

Total characters346
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 138
71.5%
None 51
 
26.4%
3 2
 
1.0%
1 2
 
1.0%

Length

2025-08-30T19:57:07.703177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:07.743352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 138
71.5%
none 51
 
26.4%
3 2
 
1.0%
1 2
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 138
39.9%
N 51
 
14.7%
o 51
 
14.7%
n 51
 
14.7%
e 51
 
14.7%
3 2
 
0.6%
1 2
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 138
39.9%
N 51
 
14.7%
o 51
 
14.7%
n 51
 
14.7%
e 51
 
14.7%
3 2
 
0.6%
1 2
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 138
39.9%
N 51
 
14.7%
o 51
 
14.7%
n 51
 
14.7%
e 51
 
14.7%
3 2
 
0.6%
1 2
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 138
39.9%
N 51
 
14.7%
o 51
 
14.7%
n 51
 
14.7%
e 51
 
14.7%
3 2
 
0.6%
1 2
 
0.6%

spinal: overall, 0 = nicht übereinstimmend, 1= übereinstimmend, 2 = MRT unklar, 3 kein MRT
Categorical

High correlation 

Distinct7
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
1
97 
None
51 
2
25 
3
10 
0
 
8
Other values (2)
 
2

Length

Max length4
Median length1
Mean length1.8186528
Min length1

Characters and Unicode

Total characters351
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 97
50.3%
None 51
26.4%
2 25
 
13.0%
3 10
 
5.2%
0 8
 
4.1%
1, 2 1
 
0.5%
0/1 1
 
0.5%

Length

2025-08-30T19:57:07.787720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:07.831530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 98
50.5%
none 51
26.3%
2 26
 
13.4%
3 10
 
5.2%
0 8
 
4.1%
0/1 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 99
28.2%
N 51
14.5%
o 51
14.5%
n 51
14.5%
e 51
14.5%
2 26
 
7.4%
3 10
 
2.8%
0 9
 
2.6%
, 1
 
0.3%
1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 351
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 99
28.2%
N 51
14.5%
o 51
14.5%
n 51
14.5%
e 51
14.5%
2 26
 
7.4%
3 10
 
2.8%
0 9
 
2.6%
, 1
 
0.3%
1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 351
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 99
28.2%
N 51
14.5%
o 51
14.5%
n 51
14.5%
e 51
14.5%
2 26
 
7.4%
3 10
 
2.8%
0 9
 
2.6%
, 1
 
0.3%
1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 351
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 99
28.2%
N 51
14.5%
o 51
14.5%
n 51
14.5%
e 51
14.5%
2 26
 
7.4%
3 10
 
2.8%
0 9
 
2.6%
, 1
 
0.3%
1
 
0.3%

initialer Fokus
Text

Distinct58
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Memory size16.8 KiB
2025-08-30T19:57:07.908085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length58
Median length47
Mean length9.5751295
Min length1

Characters and Unicode

Total characters1848
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)22.3%

Sample

1st row0
2nd rowsVorOP, Ulcus
3rd rowsVorOP
4th rowPolytox vor 20 a, Zn Diszitis
5th rowsVorOP
ValueCountFrequency (%)
none 51
16.8%
svorop 45
14.8%
kein 35
 
11.5%
z.n 26
 
8.6%
diszitis 17
 
5.6%
zn 9
 
3.0%
0 6
 
2.0%
z 4
 
1.3%
n 4
 
1.3%
urosepsis 4
 
1.3%
Other values (81) 103
33.9%
2025-08-30T19:57:08.052498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 190
 
10.3%
e 183
 
9.9%
i 162
 
8.8%
s 141
 
7.6%
o 136
 
7.4%
111
 
6.0%
r 97
 
5.2%
t 72
 
3.9%
. 60
 
3.2%
P 60
 
3.2%
Other values (50) 636
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 190
 
10.3%
e 183
 
9.9%
i 162
 
8.8%
s 141
 
7.6%
o 136
 
7.4%
111
 
6.0%
r 97
 
5.2%
t 72
 
3.9%
. 60
 
3.2%
P 60
 
3.2%
Other values (50) 636
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 190
 
10.3%
e 183
 
9.9%
i 162
 
8.8%
s 141
 
7.6%
o 136
 
7.4%
111
 
6.0%
r 97
 
5.2%
t 72
 
3.9%
. 60
 
3.2%
P 60
 
3.2%
Other values (50) 636
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 190
 
10.3%
e 183
 
9.9%
i 162
 
8.8%
s 141
 
7.6%
o 136
 
7.4%
111
 
6.0%
r 97
 
5.2%
t 72
 
3.9%
. 60
 
3.2%
P 60
 
3.2%
Other values (50) 636
34.4%

neuer Fokus nach PET
Categorical

High correlation 

Distinct42
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
kein
93 
None
51 
Zahn
 
6
HüftTEP
 
2
kein (Divertikel, unspezifisch)
 
2
Other values (37)
39 

Length

Max length67
Median length4
Mean length7.4766839
Min length1

Characters and Unicode

Total characters1443
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)18.1%

Sample

1st rowkein
2nd rowkein
3rd rowkein
4th rowkein
5th rowkein

Common Values

ValueCountFrequency (%)
kein 93
48.2%
None 51
26.4%
Zahn 6
 
3.1%
HüftTEP 2
 
1.0%
kein (Divertikel, unspezifisch) 2
 
1.0%
Knie 2
 
1.0%
Zahn (nicht erkannt) 2
 
1.0%
Divertikulitis DD Divertikulose 1
 
0.5%
Oropharynx 1
 
0.5%
- 1
 
0.5%
Other values (32) 32
 
16.6%

Length

2025-08-30T19:57:08.122585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kein 103
40.1%
none 51
19.8%
nicht 11
 
4.3%
zahn 11
 
4.3%
bestätigt 6
 
2.3%
knie 5
 
1.9%
unspezifisch 3
 
1.2%
erkannt 3
 
1.2%
abszess 3
 
1.2%
u 3
 
1.2%
Other values (48) 58
22.6%

Most occurring characters

ValueCountFrequency (%)
e 215
14.9%
n 211
14.6%
i 188
13.0%
k 116
 
8.0%
o 74
 
5.1%
t 72
 
5.0%
64
 
4.4%
s 59
 
4.1%
N 51
 
3.5%
h 35
 
2.4%
Other values (45) 358
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 215
14.9%
n 211
14.6%
i 188
13.0%
k 116
 
8.0%
o 74
 
5.1%
t 72
 
5.0%
64
 
4.4%
s 59
 
4.1%
N 51
 
3.5%
h 35
 
2.4%
Other values (45) 358
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 215
14.9%
n 211
14.6%
i 188
13.0%
k 116
 
8.0%
o 74
 
5.1%
t 72
 
5.0%
64
 
4.4%
s 59
 
4.1%
N 51
 
3.5%
h 35
 
2.4%
Other values (45) 358
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 215
14.9%
n 211
14.6%
i 188
13.0%
k 116
 
8.0%
o 74
 
5.1%
t 72
 
5.0%
64
 
4.4%
s 59
 
4.1%
N 51
 
3.5%
h 35
 
2.4%
Other values (45) 358
24.8%

TE at sus focus 2 = vorOP spinal, 0 Fokus nicht dargestellt, 3 kein Fokus, 4 Fokus weg/saniert, 5 Fokus nicht gefunden
Categorical

High correlation 

Distinct10
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
None
51 
3
46 
2
45 
1
22 
0
10 
Other values (5)
19 

Length

Max length10
Median length1
Mean length1.8860104
Min length1

Characters and Unicode

Total characters364
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
None 51
26.4%
3 46
23.8%
2 45
23.3%
1 22
11.4%
0 10
 
5.2%
4 10
 
5.2%
5 5
 
2.6%
2, 5 2
 
1.0%
3, 5 1
 
0.5%
1, 2, 2005 1
 
0.5%

Length

2025-08-30T19:57:08.168920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:08.214156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
none 51
25.8%
2 48
24.2%
3 47
23.7%
1 23
11.6%
0 10
 
5.1%
4 10
 
5.1%
5 8
 
4.0%
2005 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
N 51
14.0%
o 51
14.0%
n 51
14.0%
e 51
14.0%
2 49
13.5%
3 47
12.9%
1 23
6.3%
0 12
 
3.3%
4 10
 
2.7%
5 9
 
2.5%
Other values (2) 10
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 364
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 51
14.0%
o 51
14.0%
n 51
14.0%
e 51
14.0%
2 49
13.5%
3 47
12.9%
1 23
6.3%
0 12
 
3.3%
4 10
 
2.7%
5 9
 
2.5%
Other values (2) 10
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 364
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 51
14.0%
o 51
14.0%
n 51
14.0%
e 51
14.0%
2 49
13.5%
3 47
12.9%
1 23
6.3%
0 12
 
3.3%
4 10
 
2.7%
5 9
 
2.5%
Other values (2) 10
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 364
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 51
14.0%
o 51
14.0%
n 51
14.0%
e 51
14.0%
2 49
13.5%
3 47
12.9%
1 23
6.3%
0 12
 
3.3%
4 10
 
2.7%
5 9
 
2.5%
Other values (2) 10
 
2.7%

add TE
Categorical

High correlation  Missing 

Distinct2
Distinct (%)1.4%
Missing51
Missing (%)26.4%
Memory size15.3 KiB
0
96 
1
46 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters142
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 96
49.7%
1 46
23.8%
(Missing) 51
26.4%

Length

2025-08-30T19:57:08.266196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:08.301385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 96
67.6%
1 46
32.4%

Most occurring characters

ValueCountFrequency (%)
0 96
67.6%
1 46
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 96
67.6%
1 46
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 96
67.6%
1 46
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 96
67.6%
1 46
32.4%

add TE 1 = new focus 2=TE nicht relevant und tatsächlich nicht relevant 3 = nicht untersucht 4 = kein Fokus 5 = bekannter Fokus, schon behandelt 6 Tumor 0 no add TE
Categorical

High correlation  Missing 

Distinct9
Distinct (%)6.3%
Missing51
Missing (%)26.4%
Memory size15.3 KiB
0
93 
1
20 
4
11 
3
 
6
2
 
4
Other values (4)
 
8

Length

Max length4
Median length1
Mean length1.0422535
Min length1

Characters and Unicode

Total characters148
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.4%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 93
48.2%
1 20
 
10.4%
4 11
 
5.7%
3 6
 
3.1%
2 4
 
2.1%
5 4
 
2.1%
6 2
 
1.0%
5, 6 1
 
0.5%
4, 5 1
 
0.5%
(Missing) 51
26.4%

Length

2025-08-30T19:57:08.343764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:08.390059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 93
64.6%
1 20
 
13.9%
4 12
 
8.3%
3 6
 
4.2%
5 6
 
4.2%
2 4
 
2.8%
6 3
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 93
62.8%
1 20
 
13.5%
4 12
 
8.1%
3 6
 
4.1%
5 6
 
4.1%
2 4
 
2.7%
6 3
 
2.0%
, 2
 
1.4%
2
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 93
62.8%
1 20
 
13.5%
4 12
 
8.1%
3 6
 
4.1%
5 6
 
4.1%
2 4
 
2.7%
6 3
 
2.0%
, 2
 
1.4%
2
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 93
62.8%
1 20
 
13.5%
4 12
 
8.1%
3 6
 
4.1%
5 6
 
4.1%
2 4
 
2.7%
6 3
 
2.0%
, 2
 
1.4%
2
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 93
62.8%
1 20
 
13.5%
4 12
 
8.1%
3 6
 
4.1%
5 6
 
4.1%
2 4
 
2.7%
6 3
 
2.0%
, 2
 
1.4%
2
 
1.4%

nonspinal: overall 2 unklar 3 nicht abklärt 0 nicht übereinstimmend 4 ausgeheilt nach Behandlung 5 nicht abgebildet
Categorical

High correlation  Imbalance  Missing 

Distinct6
Distinct (%)4.2%
Missing51
Missing (%)26.4%
Memory size15.3 KiB
1
119 
0
15 
3
 
3
5
 
2
1, 0
 
2

Length

Max length4
Median length1
Mean length1.0422535
Min length1

Characters and Unicode

Total characters148
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 119
61.7%
0 15
 
7.8%
3 3
 
1.6%
5 2
 
1.0%
1, 0 2
 
1.0%
2 1
 
0.5%
(Missing) 51
26.4%

Length

2025-08-30T19:57:08.440207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:08.482872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 121
84.0%
0 17
 
11.8%
3 3
 
2.1%
5 2
 
1.4%
2 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 121
81.8%
0 17
 
11.5%
3 3
 
2.0%
5 2
 
1.4%
, 2
 
1.4%
2
 
1.4%
2 1
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 121
81.8%
0 17
 
11.5%
3 3
 
2.0%
5 2
 
1.4%
, 2
 
1.4%
2
 
1.4%
2 1
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 121
81.8%
0 17
 
11.5%
3 3
 
2.0%
5 2
 
1.4%
, 2
 
1.4%
2
 
1.4%
2 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 121
81.8%
0 17
 
11.5%
3 3
 
2.0%
5 2
 
1.4%
, 2
 
1.4%
2
 
1.4%
2 1
 
0.7%

reason for PET
Categorical

High correlation  Imbalance 

Distinct22
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
Fokussuche
104 
None
51 
Ausschluss Diszitis
12 
Fokussuche, Ausschluss Diszitis
 
4
MRT-Ersatz
 
2
Other values (17)
20 

Length

Max length68
Median length10
Mean length11.673575
Min length4

Characters and Unicode

Total characters2253
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)7.3%

Sample

1st rowFokussuche
2nd rowFokussuche
3rd rowFokussuche
4th rowAusschluss Diszitis
5th rowFokussuche

Common Values

ValueCountFrequency (%)
Fokussuche 104
53.9%
None 51
26.4%
Ausschluss Diszitis 12
 
6.2%
Fokussuche, Ausschluss Diszitis 4
 
2.1%
MRT-Ersatz 2
 
1.0%
Fokussuche, Ausschluss Diszitis, MRT Artefakt 2
 
1.0%
Fokussuche, Nachweis Diszitis 2
 
1.0%
Nachweis Diszitis 2
 
1.0%
MRT-Unfähigkeit b HSM 1
 
0.5%
MRT-Unfähigkeit b DBS 1
 
0.5%
Other values (12) 12
 
6.2%

Length

2025-08-30T19:57:08.531035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fokussuche 118
44.4%
none 51
19.2%
diszitis 30
 
11.3%
ausschluss 23
 
8.6%
mrt 6
 
2.3%
nachweis 6
 
2.3%
bei 4
 
1.5%
artefakt 3
 
1.1%
verlaufskontrolle 2
 
0.8%
b 2
 
0.8%
Other values (19) 21
 
7.9%

Most occurring characters

ValueCountFrequency (%)
s 402
17.8%
u 285
12.6%
e 203
9.0%
o 176
 
7.8%
h 153
 
6.8%
c 149
 
6.6%
k 129
 
5.7%
F 118
 
5.2%
i 111
 
4.9%
73
 
3.2%
Other values (37) 454
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2253
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 402
17.8%
u 285
12.6%
e 203
9.0%
o 176
 
7.8%
h 153
 
6.8%
c 149
 
6.6%
k 129
 
5.7%
F 118
 
5.2%
i 111
 
4.9%
73
 
3.2%
Other values (37) 454
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2253
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 402
17.8%
u 285
12.6%
e 203
9.0%
o 176
 
7.8%
h 153
 
6.8%
c 149
 
6.6%
k 129
 
5.7%
F 118
 
5.2%
i 111
 
4.9%
73
 
3.2%
Other values (37) 454
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2253
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 402
17.8%
u 285
12.6%
e 203
9.0%
o 176
 
7.8%
h 153
 
6.8%
c 149
 
6.6%
k 129
 
5.7%
F 118
 
5.2%
i 111
 
4.9%
73
 
3.2%
Other values (37) 454
20.2%

1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne Fokus
Categorical

High correlation 

Distinct10
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
1
103 
None
51 
2
17 
1, 2
11 
3
 
4
Other values (5)
 
7

Length

Max length18
Median length1
Mean length2.1709845
Min length1

Characters and Unicode

Total characters419
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.6%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 103
53.4%
None 51
26.4%
2 17
 
8.8%
1, 2 11
 
5.7%
3 4
 
2.1%
1, 3 2
 
1.0%
4 2
 
1.0%
3, 4 1
 
0.5%
3, 6, (2 kein MRT) 1
 
0.5%
2 (kein MRT), 5 1
 
0.5%

Length

2025-08-30T19:57:08.576568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:08.621780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 116
54.2%
none 51
23.8%
2 30
 
14.0%
3 8
 
3.7%
4 3
 
1.4%
kein 2
 
0.9%
mrt 2
 
0.9%
6 1
 
0.5%
5 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 116
27.7%
n 53
12.6%
e 53
12.6%
N 51
12.2%
o 51
12.2%
2 30
 
7.2%
21
 
5.0%
, 17
 
4.1%
3 8
 
1.9%
4 3
 
0.7%
Other values (9) 16
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 116
27.7%
n 53
12.6%
e 53
12.6%
N 51
12.2%
o 51
12.2%
2 30
 
7.2%
21
 
5.0%
, 17
 
4.1%
3 8
 
1.9%
4 3
 
0.7%
Other values (9) 16
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 116
27.7%
n 53
12.6%
e 53
12.6%
N 51
12.2%
o 51
12.2%
2 30
 
7.2%
21
 
5.0%
, 17
 
4.1%
3 8
 
1.9%
4 3
 
0.7%
Other values (9) 16
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 116
27.7%
n 53
12.6%
e 53
12.6%
N 51
12.2%
o 51
12.2%
2 30
 
7.2%
21
 
5.0%
, 17
 
4.1%
3 8
 
1.9%
4 3
 
0.7%
Other values (9) 16
 
3.8%

Risikofaktoren
Categorical

High correlation 

Distinct41
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
None
51 
0
37 
3
15 
1
14 
1, 3
12 
Other values (36)
64 

Length

Max length13
Median length10
Mean length3.2331606
Min length1

Characters and Unicode

Total characters624
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)11.9%

Sample

1st row1
2nd row4
3rd row7.8
4th row2
5th row0

Common Values

ValueCountFrequency (%)
None 51
26.4%
0 37
19.2%
3 15
 
7.8%
1 14
 
7.3%
1, 3 12
 
6.2%
4 6
 
3.1%
2 5
 
2.6%
7, 8 5
 
2.6%
8 4
 
2.1%
3, 7, 2008 4
 
2.1%
Other values (31) 40
20.7%

Length

2025-08-30T19:57:08.674963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 51
19.2%
3 47
17.7%
0 37
14.0%
1 36
13.6%
8 16
 
6.0%
4 14
 
5.3%
7 14
 
5.3%
6 11
 
4.2%
2008 7
 
2.6%
9 6
 
2.3%
Other values (11) 26
9.8%

Most occurring characters

ValueCountFrequency (%)
, 72
11.5%
72
11.5%
0 63
10.1%
e 54
8.7%
o 52
8.3%
N 51
8.2%
n 51
8.2%
1 50
8.0%
3 48
7.7%
8 24
 
3.8%
Other values (14) 87
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 72
11.5%
72
11.5%
0 63
10.1%
e 54
8.7%
o 52
8.3%
N 51
8.2%
n 51
8.2%
1 50
8.0%
3 48
7.7%
8 24
 
3.8%
Other values (14) 87
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 72
11.5%
72
11.5%
0 63
10.1%
e 54
8.7%
o 52
8.3%
N 51
8.2%
n 51
8.2%
1 50
8.0%
3 48
7.7%
8 24
 
3.8%
Other values (14) 87
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 72
11.5%
72
11.5%
0 63
10.1%
e 54
8.7%
o 52
8.3%
N 51
8.2%
n 51
8.2%
1 50
8.0%
3 48
7.7%
8 24
 
3.8%
Other values (14) 87
13.9%

RevisionsOP 2 =kein Infekt
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)2.1%
Missing51
Missing (%)26.4%
Memory size15.6 KiB
0.0
120 
1.0
 
12
2.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters426
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 120
62.2%
1.0 12
 
6.2%
2.0 10
 
5.2%
(Missing) 51
26.4%

Length

2025-08-30T19:57:08.715859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:08.753421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 120
84.5%
1.0 12
 
8.5%
2.0 10
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 262
61.5%
. 142
33.3%
1 12
 
2.8%
2 10
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 262
61.5%
. 142
33.3%
1 12
 
2.8%
2 10
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 262
61.5%
. 142
33.3%
1 12
 
2.8%
2 10
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 262
61.5%
. 142
33.3%
1 12
 
2.8%
2 10
 
2.3%

ASA
Categorical

High correlation  Missing 

Distinct4
Distinct (%)2.8%
Missing51
Missing (%)26.4%
Memory size15.6 KiB
3.0
87 
2.0
46 
4.0
 
7
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters426
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0 87
45.1%
2.0 46
23.8%
4.0 7
 
3.6%
1.0 2
 
1.0%
(Missing) 51
26.4%

Length

2025-08-30T19:57:08.793157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:08.831286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3.0 87
61.3%
2.0 46
32.4%
4.0 7
 
4.9%
1.0 2
 
1.4%

Most occurring characters

ValueCountFrequency (%)
. 142
33.3%
0 142
33.3%
3 87
20.4%
2 46
 
10.8%
4 7
 
1.6%
1 2
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 142
33.3%
0 142
33.3%
3 87
20.4%
2 46
 
10.8%
4 7
 
1.6%
1 2
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 142
33.3%
0 142
33.3%
3 87
20.4%
2 46
 
10.8%
4 7
 
1.6%
1 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 142
33.3%
0 142
33.3%
3 87
20.4%
2 46
 
10.8%
4 7
 
1.6%
1 2
 
0.5%

ausgeheilt 2=NA 3=dead
Categorical

High correlation  Missing 

Distinct7
Distinct (%)4.9%
Missing51
Missing (%)26.4%
Memory size15.3 KiB
1
68 
2
48 
x
12 
3
8 
0
 
4
Other values (2)
 
2

Length

Max length3
Median length1
Mean length1.0211268
Min length1

Characters and Unicode

Total characters145
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.4%

Sample

1st row1
2nd row1
3rd row1
4th rowx
5th row1

Common Values

ValueCountFrequency (%)
1 68
35.2%
2 48
24.9%
x 12
 
6.2%
3 8
 
4.1%
0 4
 
2.1%
x/1 1
 
0.5%
1? 1
 
0.5%
(Missing) 51
26.4%

Length

2025-08-30T19:57:08.878635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:08.924858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 69
48.6%
2 48
33.8%
x 12
 
8.5%
3 8
 
5.6%
0 4
 
2.8%
x/1 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 70
48.3%
2 48
33.1%
x 13
 
9.0%
3 8
 
5.5%
0 4
 
2.8%
/ 1
 
0.7%
? 1
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 70
48.3%
2 48
33.1%
x 13
 
9.0%
3 8
 
5.5%
0 4
 
2.8%
/ 1
 
0.7%
? 1
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 70
48.3%
2 48
33.1%
x 13
 
9.0%
3 8
 
5.5%
0 4
 
2.8%
/ 1
 
0.7%
? 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 70
48.3%
2 48
33.1%
x 13
 
9.0%
3 8
 
5.5%
0 4
 
2.8%
/ 1
 
0.7%
? 1
 
0.7%

Neurologie 1 = Paresen, 2 = vorbestehend, 3 = Tetraparese
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
None
162 
0.0
24 
2.0
 
5
1.0
 
1
3.0
 
1

Length

Max length4
Median length4
Mean length3.8393782
Min length3

Characters and Unicode

Total characters741
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 162
83.9%
0.0 24
 
12.4%
2.0 5
 
2.6%
1.0 1
 
0.5%
3.0 1
 
0.5%

Length

2025-08-30T19:57:08.971586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:09.011630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
none 162
83.9%
0.0 24
 
12.4%
2.0 5
 
2.6%
1.0 1
 
0.5%
3.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
N 162
21.9%
o 162
21.9%
n 162
21.9%
e 162
21.9%
0 55
 
7.4%
. 31
 
4.2%
2 5
 
0.7%
1 1
 
0.1%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 741
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 162
21.9%
o 162
21.9%
n 162
21.9%
e 162
21.9%
0 55
 
7.4%
. 31
 
4.2%
2 5
 
0.7%
1 1
 
0.1%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 741
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 162
21.9%
o 162
21.9%
n 162
21.9%
e 162
21.9%
0 55
 
7.4%
. 31
 
4.2%
2 5
 
0.7%
1 1
 
0.1%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 741
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 162
21.9%
o 162
21.9%
n 162
21.9%
e 162
21.9%
0 55
 
7.4%
. 31
 
4.2%
2 5
 
0.7%
1 1
 
0.1%
3 1
 
0.1%

Besserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues Defizit
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)10.7%
Missing165
Missing (%)85.5%
Memory size16.0 KiB
x
26 
2
 
1
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)7.1%

Sample

1st row2
2nd rowx
3rd rowx
4th row0
5th rowx

Common Values

ValueCountFrequency (%)
x 26
 
13.5%
2 1
 
0.5%
0 1
 
0.5%
(Missing) 165
85.5%

Length

2025-08-30T19:57:09.054349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T19:57:09.087891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
x 26
92.9%
2 1
 
3.6%
0 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
x 26
92.9%
2 1
 
3.6%
0 1
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
x 26
92.9%
2 1
 
3.6%
0 1
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
x 26
92.9%
2 1
 
3.6%
0 1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
x 26
92.9%
2 1
 
3.6%
0 1
 
3.6%

(1= DM, 2=iv Drogen, 3=PAVK, 4=Cortisontherapie, 5=Zahnbehandlung, 6=Immunschwäche, 7=zn CTX, 8=Malignom, 9=Infiltrationen, 10=C2, 11=Parkinson, 12 = Niereninsuffizienz)
Text

Missing 

Distinct6
Distinct (%)75.0%
Missing185
Missing (%)95.9%
Memory size12.1 KiB
2025-08-30T19:57:09.131774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length60
Median length38
Mean length27.875
Min length13

Characters and Unicode

Total characters223
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)62.5%

Sample

1st rowMRT im Verlauf in Narkose postop ergänzt
2nd rowvorbestehender sensibler Querschnitt
3rd row3x PET für Struma, 1x PET danach Ortho Ausschluss Entzündung
4th row1x Myokardszinti davor
5th row5x NUK (SD, Knochendichte)
ValueCountFrequency (%)
1x 5
 
13.9%
spect 3
 
8.3%
herz 3
 
8.3%
pet 2
 
5.6%
für 1
 
2.8%
sd 1
 
2.8%
nuk 1
 
2.8%
5x 1
 
2.8%
davor 1
 
2.8%
myokardszinti 1
 
2.8%
Other values (17) 17
47.2%
2025-08-30T19:57:09.239065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28
 
12.6%
e 18
 
8.1%
r 15
 
6.7%
t 13
 
5.8%
n 12
 
5.4%
s 11
 
4.9%
h 10
 
4.5%
c 8
 
3.6%
o 8
 
3.6%
x 7
 
3.1%
Other values (36) 93
41.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 223
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
28
 
12.6%
e 18
 
8.1%
r 15
 
6.7%
t 13
 
5.8%
n 12
 
5.4%
s 11
 
4.9%
h 10
 
4.5%
c 8
 
3.6%
o 8
 
3.6%
x 7
 
3.1%
Other values (36) 93
41.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 223
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
28
 
12.6%
e 18
 
8.1%
r 15
 
6.7%
t 13
 
5.8%
n 12
 
5.4%
s 11
 
4.9%
h 10
 
4.5%
c 8
 
3.6%
o 8
 
3.6%
x 7
 
3.1%
Other values (36) 93
41.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 223
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
28
 
12.6%
e 18
 
8.1%
r 15
 
6.7%
t 13
 
5.8%
n 12
 
5.4%
s 11
 
4.9%
h 10
 
4.5%
c 8
 
3.6%
o 8
 
3.6%
x 7
 
3.1%
Other values (36) 93
41.7%

Unnamed: 45
Text

Missing 

Distinct12
Distinct (%)66.7%
Missing175
Missing (%)90.7%
Memory size12.8 KiB
2025-08-30T19:57:09.311927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length67
Median length56
Mean length29.666667
Min length9

Characters and Unicode

Total characters534
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)50.0%

Sample

1st rowein PET Onko zusätzlich davor
2nd rowein PET Onko zusätzlich davor
3rd rowDD Rheuma
4th rowRediszitis
5th row1x HerzSPECT
ValueCountFrequency (%)
pet 7
 
8.2%
davor 6
 
7.1%
1x 5
 
5.9%
erkannt 5
 
5.9%
herzspect 4
 
4.7%
nicht 4
 
4.7%
diszitis 4
 
4.7%
zähne 3
 
3.5%
keine 3
 
3.5%
ein 3
 
3.5%
Other values (29) 41
48.2%
2025-08-30T19:57:09.428892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67
 
12.5%
i 49
 
9.2%
n 40
 
7.5%
e 32
 
6.0%
t 27
 
5.1%
r 22
 
4.1%
s 21
 
3.9%
a 21
 
3.9%
E 18
 
3.4%
h 18
 
3.4%
Other values (37) 219
41.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 534
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
67
 
12.5%
i 49
 
9.2%
n 40
 
7.5%
e 32
 
6.0%
t 27
 
5.1%
r 22
 
4.1%
s 21
 
3.9%
a 21
 
3.9%
E 18
 
3.4%
h 18
 
3.4%
Other values (37) 219
41.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 534
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
67
 
12.5%
i 49
 
9.2%
n 40
 
7.5%
e 32
 
6.0%
t 27
 
5.1%
r 22
 
4.1%
s 21
 
3.9%
a 21
 
3.9%
E 18
 
3.4%
h 18
 
3.4%
Other values (37) 219
41.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 534
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
67
 
12.5%
i 49
 
9.2%
n 40
 
7.5%
e 32
 
6.0%
t 27
 
5.1%
r 22
 
4.1%
s 21
 
3.9%
a 21
 
3.9%
E 18
 
3.4%
h 18
 
3.4%
Other values (37) 219
41.0%

Unnamed: 46
Text

Missing 

Distinct5
Distinct (%)100.0%
Missing188
Missing (%)97.4%
Memory size12.0 KiB
2025-08-30T19:57:09.495922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length69
Median length54
Mean length48.2
Min length25

Characters and Unicode

Total characters241
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st rowPET hat Diszitis verkannt
2nd rowPET hat Beine nicht abgebildet, wo Eintrittspforte war
3rd rowPET hat keine Diszitis, intraop eher eitrig, Patho/Mibi unauffällig
4th rowEndokarditis nicht erkannt
5th rowkeine Mibi/Patho, PET sagt Osteochondrose, MRT und wir sagen Diszitis
ValueCountFrequency (%)
pet 4
 
11.8%
diszitis 3
 
8.8%
hat 3
 
8.8%
nicht 2
 
5.9%
keine 2
 
5.9%
unauffällig 1
 
2.9%
wir 1
 
2.9%
und 1
 
2.9%
mrt 1
 
2.9%
osteochondrose 1
 
2.9%
Other values (15) 15
44.1%
2025-08-30T19:57:09.599329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29
 
12.0%
i 28
 
11.6%
t 22
 
9.1%
e 17
 
7.1%
n 16
 
6.6%
a 14
 
5.8%
s 12
 
5.0%
r 11
 
4.6%
o 9
 
3.7%
h 9
 
3.7%
Other values (23) 74
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 241
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
29
 
12.0%
i 28
 
11.6%
t 22
 
9.1%
e 17
 
7.1%
n 16
 
6.6%
a 14
 
5.8%
s 12
 
5.0%
r 11
 
4.6%
o 9
 
3.7%
h 9
 
3.7%
Other values (23) 74
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 241
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
29
 
12.0%
i 28
 
11.6%
t 22
 
9.1%
e 17
 
7.1%
n 16
 
6.6%
a 14
 
5.8%
s 12
 
5.0%
r 11
 
4.6%
o 9
 
3.7%
h 9
 
3.7%
Other values (23) 74
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 241
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
29
 
12.0%
i 28
 
11.6%
t 22
 
9.1%
e 17
 
7.1%
n 16
 
6.6%
a 14
 
5.8%
s 12
 
5.0%
r 11
 
4.6%
o 9
 
3.7%
h 9
 
3.7%
Other values (23) 74
30.7%

Interactions

2025-08-30T19:57:03.344004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-30T19:57:01.962161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-30T19:57:02.681870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-30T19:57:03.624841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-30T19:57:02.091963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-30T19:57:03.053789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-30T19:57:03.772426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-30T19:57:02.420168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-30T19:57:03.163382image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-08-30T19:57:09.660184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne Fokus1 = lowgrade 2 = highgradeASABWSBesserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues DefizitCRP initialFokus abgeklärtHWSLWSNeurologie 1 = Paresen, 2 = vorbestehend, 3 = TetrapareseRevisionsOP 2 =kein InfektRisikofaktorenTE (at all)TE at sus focus 2 = vorOP spinal, 0 Fokus nicht dargestellt, 3 kein Fokus, 4 Fokus weg/saniert, 5 Fokus nicht gefundenUnnamed: 0add TEadd TE 1 = new focus 2=TE nicht relevant und tatsächlich nicht relevant 3 = nicht untersucht 4 = kein Fokus 5 = bekannter Fokus, schon behandelt 6 Tumor 0 no add TEageausgeheilt 2=NA 3=deadbiopsydiscitis in MRT = TE, 2 Frage diszitis b MRT unklar, 0 = n übereinstimmend, 3 kein MRT, 4 Ausschluss Diszitis, 5 Diszitis im MRT n erkannt, 6 neuer Nachweis Diszitishisto surgery 3 intermediär 0 negintraspinalmibi othermibi surgery 0 = negneuer Fokus nach PETnonspinal: overall 2 unklar 3 nicht abklärt 0 nicht übereinstimmend 4 ausgeheilt nach Behandlung 5 nicht abgebildetother spinal TEreason for PETsex (1F, 2M)spinal: overall, 0 = nicht übereinstimmend, 1= übereinstimmend, 2 = MRT unklar, 3 kein MRTunspez Fokus abgeklärt 0nein 1ja+neg 2ja+posunspez gewertetweitere
1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne Fokus1.0000.3930.0000.2810.6190.0000.0000.2740.2750.0840.0000.4170.7780.3640.0880.1280.0000.2240.0460.1750.5630.4090.2450.1110.0000.0000.0000.6780.8080.0000.6360.1770.0000.000
1 = lowgrade 2 = highgrade0.3931.0000.0000.1660.0000.2670.0570.1630.1990.0000.0000.3370.2390.2880.1450.1820.0000.0000.0880.1190.3570.3970.2050.3280.7070.2040.0000.4070.6320.0980.3570.1040.1510.071
ASA0.0000.0001.0000.1350.0000.1740.0000.1520.2100.0000.0000.2830.0000.0001.0000.0000.0000.2780.3960.0420.1420.2210.0590.0000.1770.2690.0000.0000.0000.0000.0000.2750.4340.000
BWS0.2810.1660.1351.0000.1790.0000.0000.7070.7360.0000.0000.0330.0000.2810.1020.0000.0000.0000.0000.6760.2720.2750.7030.0000.2010.0000.0590.3080.2100.1890.2920.0000.0720.085
Besserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues Defizit0.6190.0000.0000.1791.0000.3460.0000.2680.1230.7150.0000.2820.6510.0451.0000.0000.0000.0000.0000.0000.0000.4270.0000.0000.6320.3380.0001.0000.6190.1790.0001.0001.0000.000
CRP initial0.0000.2670.1740.0000.3461.0000.3080.2340.0000.0000.0000.0000.0000.0980.0300.2140.2910.1220.0000.1270.0000.1570.0000.1230.2520.2250.4040.2430.2780.0000.0000.1470.4030.171
Fokus abgeklärt0.0000.0570.0000.0000.0000.3081.0000.0780.0000.0000.0450.2710.0820.1190.0860.6040.3200.3160.0000.0000.0000.0000.0110.0000.0000.6450.1510.3920.0000.0000.0000.0000.0000.000
HWS0.2740.1630.1520.7070.2680.2340.0781.0000.7430.0550.0000.2190.0000.2940.1020.2570.3470.0000.0000.6830.3010.2670.7090.1900.0000.2620.0000.3090.1070.1940.2880.1460.2350.076
LWS0.2750.1990.2100.7360.1230.0000.0000.7431.0000.0550.0670.1430.0000.3020.1020.0000.1720.0000.0000.6750.2920.2940.6740.1190.1550.1220.0280.3130.2600.1880.2970.0000.0880.000
Neurologie 1 = Paresen, 2 = vorbestehend, 3 = Tetraparese0.0840.0000.0000.0000.7150.0000.0000.0550.0551.0000.0000.6610.0000.0000.1450.0000.0000.0000.0000.2040.0000.0000.0000.3150.0000.4180.0000.0680.0000.0000.0270.0000.0000.000
RevisionsOP 2 =kein Infekt0.0000.0000.0000.0000.0000.0000.0450.0000.0670.0001.0000.0000.0000.1061.0000.0540.0000.2140.3880.0000.2510.0840.0000.0310.2990.0000.0000.0000.3270.0000.0000.1550.0480.121
Risikofaktoren0.4170.3370.2830.0330.2820.0000.2710.2190.1430.6610.0001.0000.0000.3080.4080.0000.1230.1930.4390.0000.5070.4860.0000.3870.0000.3450.0000.8040.2810.0000.5800.5630.2660.252
TE (at all)0.7780.2390.0000.0000.6510.0000.0820.0000.0000.0000.0000.0001.0000.2041.0000.2550.0000.0000.2440.0000.3800.4010.1680.0000.0000.0000.0000.0000.3300.1320.3740.0000.0000.000
TE at sus focus 2 = vorOP spinal, 0 Fokus nicht dargestellt, 3 kein Fokus, 4 Fokus weg/saniert, 5 Fokus nicht gefunden0.3640.2880.0000.2810.0450.0980.1190.2940.3020.0000.1060.3080.2041.0000.1620.2580.3540.1470.0000.1820.3900.3970.2410.4960.1230.4310.4350.5520.3550.0000.4110.0000.0000.000
Unnamed: 00.0880.1451.0000.1021.0000.0300.0860.1020.1020.1451.0000.4081.0000.1621.0001.0001.000-0.0261.0000.1450.0000.0820.1250.4451.0000.4471.0000.0470.2410.0000.0380.1620.2610.261
add TE0.1280.1820.0000.0000.0000.2140.6040.2570.0000.0000.0540.0000.2550.2581.0001.0000.9320.0000.1230.1270.0000.0000.0000.2250.2870.6960.2770.0000.0000.0000.0000.2030.2280.000
add TE 1 = new focus 2=TE nicht relevant und tatsächlich nicht relevant 3 = nicht untersucht 4 = kein Fokus 5 = bekannter Fokus, schon behandelt 6 Tumor 0 no add TE0.0000.0000.0000.0000.0000.2910.3200.3470.1720.0000.0000.1230.0000.3541.0000.9321.0000.2030.0000.0000.0000.0000.0000.3830.2370.7250.5160.1450.0000.0590.0000.3350.2720.000
age0.2240.0000.2780.0000.0000.1220.3160.0000.0000.0000.2140.1930.0000.147-0.0260.0000.2031.0000.0000.0000.0710.2560.0000.1230.0000.1910.4300.0000.3130.2110.0000.0000.0000.000
ausgeheilt 2=NA 3=dead0.0460.0880.3960.0000.0000.0000.0000.0000.0000.0000.3880.4390.2440.0001.0000.1230.0000.0001.0000.0000.4440.3430.0000.0000.0000.0000.1650.0000.0000.0000.4940.0000.0000.000
biopsy0.1750.1190.0420.6760.0000.1270.0000.6830.6750.2040.0000.0000.0000.1820.1450.1270.0000.0000.0001.0000.1360.1780.5480.0000.0000.0000.0000.2620.0000.1620.1970.0000.0000.000
discitis in MRT = TE, 2 Frage diszitis b MRT unklar, 0 = n übereinstimmend, 3 kein MRT, 4 Ausschluss Diszitis, 5 Diszitis im MRT n erkannt, 6 neuer Nachweis Diszitis0.5630.3570.1420.2720.0000.0000.0000.3010.2920.0000.2510.5070.3800.3900.0000.0000.0000.0710.4440.1361.0000.4870.2610.3380.0000.2780.0000.6000.6060.0000.8450.4370.2640.085
histo surgery 3 intermediär 0 neg0.4090.3970.2210.2750.4270.1570.0000.2670.2940.0000.0840.4860.4010.3970.0820.0000.0000.2560.3430.1780.4871.0000.2750.5870.4400.2450.3590.5490.3830.0000.4950.1660.2250.066
intraspinal0.2450.2050.0590.7030.0000.0000.0110.7090.6740.0000.0000.0000.1680.2410.1250.0000.0000.0000.0000.5480.2610.2751.0000.0000.0000.0000.0000.2460.0630.1840.2750.0000.2900.000
mibi other0.1110.3280.0000.0000.0000.1230.0000.1900.1190.3150.0310.3870.0000.4960.4450.2250.3830.1230.0000.0000.3380.5870.0001.0000.4390.5410.0000.2900.1530.0000.1750.3590.3870.261
mibi surgery 0 = neg0.0000.7070.1770.2010.6320.2520.0000.0000.1550.0000.2990.0000.0000.1231.0000.2870.2370.0000.0000.0000.0000.4400.0000.4391.0000.2750.6410.0000.0000.0000.0000.0000.0000.466
neuer Fokus nach PET0.0000.2040.2690.0000.3380.2250.6450.2620.1220.4180.0000.3450.0000.4310.4470.6960.7250.1910.0000.0000.2780.2450.0000.5410.2751.0000.3150.7930.0000.0000.2600.6760.4910.000
nonspinal: overall 2 unklar 3 nicht abklärt 0 nicht übereinstimmend 4 ausgeheilt nach Behandlung 5 nicht abgebildet0.0000.0000.0000.0590.0000.4040.1510.0000.0280.0000.0000.0000.0000.4351.0000.2770.5160.4300.1650.0000.0000.3590.0000.0000.6410.3151.0000.0000.0000.1300.0000.0110.2840.225
other spinal TE0.6780.4070.0000.3081.0000.2430.3920.3090.3130.0680.0000.8040.0000.5520.0470.0000.1450.0000.0000.2620.6000.5490.2460.2900.0000.7930.0001.0000.6940.0000.6110.3840.3620.000
reason for PET0.8080.6320.0000.2100.6190.2780.0000.1070.2600.0000.3270.2810.3300.3550.2410.0000.0000.3130.0000.0000.6060.3830.0630.1530.0000.0000.0000.6941.0000.0000.7000.1930.0000.000
sex (1F, 2M)0.0000.0980.0000.1890.1790.0000.0000.1940.1880.0000.0000.0000.1320.0000.0000.0000.0590.2110.0000.1620.0000.0000.1840.0000.0000.0000.1300.0000.0001.0000.0000.0000.0000.683
spinal: overall, 0 = nicht übereinstimmend, 1= übereinstimmend, 2 = MRT unklar, 3 kein MRT0.6360.3570.0000.2920.0000.0000.0000.2880.2970.0270.0000.5800.3740.4110.0380.0000.0000.0000.4940.1970.8450.4950.2750.1750.0000.2600.0000.6110.7000.0001.0000.0690.0000.000
unspez Fokus abgeklärt 0nein 1ja+neg 2ja+pos0.1770.1040.2750.0001.0000.1470.0000.1460.0000.0000.1550.5630.0000.0000.1620.2030.3350.0000.0000.0000.4370.1660.0000.3590.0000.6760.0110.3840.1930.0000.0691.0000.8330.400
unspez gewertet0.0000.1510.4340.0721.0000.4030.0000.2350.0880.0000.0480.2660.0000.0000.2610.2280.2720.0000.0000.0000.2640.2250.2900.3870.0000.4910.2840.3620.0000.0000.0000.8331.0000.508
weitere0.0000.0710.0000.0850.0000.1710.0000.0760.0000.0000.1210.2520.0000.0000.2610.0000.0000.0000.0000.0000.0850.0660.0000.2610.4660.0000.2250.0000.0000.6830.0000.4000.5081.000

Missing values

2025-08-30T19:57:04.107397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-30T19:57:04.351723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0nameDOBageFokus abgeklärtunspez gewertetunspez Fokus abgeklärt 0nein 1ja+neg 2ja+posweitereThrombussex (1F, 2M)DiagnoseLWSBWSHWSintraspinalbiopsyOPDOAsurgery datedate of PETmibi surgery 0 = neg1 = lowgrade 2 = highgradehisto surgery 3 intermediär 0 negmibi otherCRP initialPETCT TETE (at all)discitis in MRT = TE, 2 Frage diszitis b MRT unklar, 0 = n übereinstimmend, 3 kein MRT, 4 Ausschluss Diszitis, 5 Diszitis im MRT n erkannt, 6 neuer Nachweis Diszitisother spinal TEspinal: overall, 0 = nicht übereinstimmend, 1= übereinstimmend, 2 = MRT unklar, 3 kein MRTinitialer Fokusneuer Fokus nach PETTE at sus focus 2 = vorOP spinal, 0 Fokus nicht dargestellt, 3 kein Fokus, 4 Fokus weg/saniert, 5 Fokus nicht gefundenadd TEadd TE 1 = new focus 2=TE nicht relevant und tatsächlich nicht relevant 3 = nicht untersucht 4 = kein Fokus 5 = bekannter Fokus, schon behandelt 6 Tumor 0 no add TEnonspinal: overall 2 unklar 3 nicht abklärt 0 nicht übereinstimmend 4 ausgeheilt nach Behandlung 5 nicht abgebildetreason for PET1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne FokusRisikofaktorenRevisionsOP 2 =kein InfektASAausgeheilt 2=NA 3=deadNeurologie 1 = Paresen, 2 = vorbestehend, 3 = TetrapareseBesserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues Defizit(1= DM, 2=iv Drogen, 3=PAVK, 4=Cortisontherapie, 5=Zahnbehandlung, 6=Immunschwäche, 7=zn CTX, 8=Malignom, 9=Infiltrationen, 10=C2, 11=Parkinson, 12 = Niereninsuffizienz)Unnamed: 45Unnamed: 46
01069341Rankel, Christine7/11/196556----NaN1Spondylodiszitis TH5/601000Solera TH4-5-6-7 + Deko+BSF-Ausräumung4/4/202230/5/2022NaNStreptococcus dysgalactiae, Staphylococcus capitis21None3.6NaN11010kein3001Fokussuche111.03.01NoneNaNNaNNaNNaN
11121310Mentzel, Frank17/9/195467---Ulcus, bekanntNaN2Diszitis L4/510000Solera LW2-3-S2-IA, ALIF L4/518/2/20223/3/202222/2/2022Staphylococcus epidermidis, Staphylococcus warneri11-11.2NaN1101sVorOP, Ulcuskein2001Fokussuche142.03.01NoneNaNNaNein PET Onko zusätzlich davorNaN
31187375Vial, Renee10/3/195072---BronchialCANaN1Spondylodiszitis L4/5/S1+Empyem10010Solera L4-5-S1+Deko, ALIF L4/5, 5/S121/12/202222/12/2022NaNStaphylococcus epidermidis11-2.9NaN1101sVorOPkein2001Fokussuche17.80.02.01NoneNaNNaNein PET Onko zusätzlich davorNaN
41202379Mayr, Daniela8/5/198438---Zn PankreatitisNaN1Ausschluss Diszitis11001-21/10/2022-NaNnot doneNonenot done-12NaN0202Polytox vor 20 a, Zn Diszitiskein3001Ausschluss Diszitis220.03.0xNoneNaNNaNNaNNaN
51227623Zellner, Leonhard12/9/194775----12WHST10000Verlängerung L2-3-4-5-S1-S2IA31/7/20231/8/2023NaNStaphylococcus epidermidis11-13.7NaN1101sVorOPkein2001Fokussuche100.02.01NoneNaNNaNNaNNaN
61266244Hinrichs, Holger17/4/195369-Colitis0-12Spondylodiszitis LW2/3 und LW3/4, Z. n. Solera TH 12-L1-L4-L5+WKE10000ME, WKE L2-4, Solera TH9-10-11-L5-SIA9/8/202211/8/2022NaNSerratia marcescens, Serratia nematodiphilia21-5.9NaN1101Zn Diszitis nach Erysipel US reSinusitis (u), Colitis (u)2121Fokussuche110.04.02NoneNaNNaNNaNNaN
71267203Gamon, Ingrid15/6/193982-HSM1-NaN1Schraubenlockerung L4 bds. b Z. n. Cosmic L4-5-S1NoneNoneNoneNoneNoneNoneNaNNaNNaNNaNNoneNoneNoneNaNNaNNaNNoneNoneNoneNoneNoneNoneNaNNaNNaNNoneNoneNoneNaNNaNNaNNoneNaNNaNNaNNaN
91275982Ghellere, Renato18/6/1934881---NaN2Spondylodiszitis L1-L210000OSS17/12/20222/1/202322/12/2022Staphylococcus aureus22-17.3NaN10000Pneumonie (r), Kolitis (r)3133Fokussuche110.03.02NoneNaNNaNNaNPET hat Diszitis verkannt
101369728Meier-Meitinger, Maximilian10/3/198340----NaN2Spondylodiszitis L5/S110001Solera L5-S1+Deko, ALIF17/5/202322/5/20231/6/2023Staphylococcus aureus, Cutibacterium (Propionibacterium) acnes21-13.3NaN1101Hautverletzung mit rostigem Nagel vor 2 Wochenkein0005Fokussuche100.02.01NoneNaNNaNNaNPET hat Beine nicht abgebildet, wo Eintrittspforte war
111381129Achatz, Heidi4/9/194181-kleine Gelenke, ae rheumatologisch0NeurinomNaN1Spondylodiszitis C5/600100ACDF C5/63/12/20227/12/202215/12/2022003-16.4NaN1000Urokein0125Fokussuche17, 80.03.01NoneNaNNaNDD RheumaPET hat keine Diszitis, intraop eher eitrig, Patho/Mibi unauffällig
Unnamed: 0nameDOBageFokus abgeklärtunspez gewertetunspez Fokus abgeklärt 0nein 1ja+neg 2ja+posweitereThrombussex (1F, 2M)DiagnoseLWSBWSHWSintraspinalbiopsyOPDOAsurgery datedate of PETmibi surgery 0 = neg1 = lowgrade 2 = highgradehisto surgery 3 intermediär 0 negmibi otherCRP initialPETCT TETE (at all)discitis in MRT = TE, 2 Frage diszitis b MRT unklar, 0 = n übereinstimmend, 3 kein MRT, 4 Ausschluss Diszitis, 5 Diszitis im MRT n erkannt, 6 neuer Nachweis Diszitisother spinal TEspinal: overall, 0 = nicht übereinstimmend, 1= übereinstimmend, 2 = MRT unklar, 3 kein MRTinitialer Fokusneuer Fokus nach PETTE at sus focus 2 = vorOP spinal, 0 Fokus nicht dargestellt, 3 kein Fokus, 4 Fokus weg/saniert, 5 Fokus nicht gefundenadd TEadd TE 1 = new focus 2=TE nicht relevant und tatsächlich nicht relevant 3 = nicht untersucht 4 = kein Fokus 5 = bekannter Fokus, schon behandelt 6 Tumor 0 no add TEnonspinal: overall 2 unklar 3 nicht abklärt 0 nicht übereinstimmend 4 ausgeheilt nach Behandlung 5 nicht abgebildetreason for PET1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne FokusRisikofaktorenRevisionsOP 2 =kein InfektASAausgeheilt 2=NA 3=deadNeurologie 1 = Paresen, 2 = vorbestehend, 3 = TetrapareseBesserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues Defizit(1= DM, 2=iv Drogen, 3=PAVK, 4=Cortisontherapie, 5=Zahnbehandlung, 6=Immunschwäche, 7=zn CTX, 8=Malignom, 9=Infiltrationen, 10=C2, 11=Parkinson, 12 = Niereninsuffizienz)Unnamed: 45Unnamed: 46
3362747133Girgis, Raafat Riad Fayek21/5/1953660---NaN2Spondylodiszitis BW1-2-301000Neon C6-7-Th4-5, Laminektomie C7, Th1-3 +WK BW1-3 ex, zweiter Schritt WKE/Fibula20/1/202022.01.2020NaNNaNNoneNoneNoneNaNNaNNaNNoneNoneNoneNoneNoneNoneNaNNaNNaNNoneNoneNoneNaNNaNNaNNoneNaNNaNNaNNaN
3382716417Huber, Hans-Jürgen21/4/196455----NaN2Spondylitis BW10/11 DD Tumor01001Solera Th9-10-11-1212/9/20197/11/201917/9/2019Staphylococcus saccharolyticus, Cutibacterium (Propionibacterium) acnes01STAU, Staphylococcus capitis (BK)4.2NaN1101Z.n. Appendizitiskein4001Fokussuche, Nachweis Diszitis1, 241.03.010.0xNaNNaNNaN
3392702692Schwarz, Ursula15/9/1946721---NaN2Spondylodiszitis BW12/L101001Solera BWK10-11-L2-3, WKE BWK12/l112/7/201916.07.2019NaNNaNNoneNoneNoneNaNNaNNaNNoneNoneNoneNoneNoneNoneNaNNaNNaNNoneNoneNoneNaNNaNNaNNoneNaNNaNNaNNaN
34121655353Karasova, Simona10/4/199424----NaN1Ausschluss Infektfokus, Zn OP L2 + L3-Berstungsfraktur Fraktur, Os Sacrum Fraktur100001. 3D Röntgen navigierte Stabilisierung von dorsal Th 12-L1-L4-L5 (Solera), Dekompression via Laminektomie LW 2-3 Teillaminektomie LW4, Versorgung eines traumatischen Duralecks Höhe L3/4 bdsNaNNaNNaNNaNNoneNoneNoneNaNNaNNaNNoneNoneNoneNoneNoneNoneNaNNaNNaNNoneNoneNoneNaNNaNNaNNoneNaNNaNNaNNaN
3501110893Möller, Gerhard8/6/1946720--ED ProstataCANaN2Spondylodiszitis LWK5/SWK110011Solera L4-5-S1, Laminektomie LW4,5, Facettenektomie LW4/5 beidseits, Ausräumung epidurales Empyem und Probeentnahme LWK5, Diskektomie LWK5/SW1 und interkorporelle Spondylodese mit autologem Knochen, CT-navigierte Verlängerung der bestehenden Solera Stabilisierung auf LWK3 beidseits, Dekompression über Laminektomie LW3, Neurolyse der L5 Wurzel15/12/201828.12.2018NaNNaNNoneNoneNoneNaNNaNNaNNoneNoneNoneNoneNoneNoneNaNNaNNaNNoneNoneNoneNaNNaNNaNNoneNaNNaNNaNNaN
3542622568Kett, Margarete9/2/193187----NaN1Spondylodiszitis BW12-LW1-LW1/2 beiNoneNoneNoneNoneNoneNoneNaNNaNNaNNaNNoneNoneNoneNaNNaNNaNNoneNoneNoneNoneNoneNoneNaNNaNNaNNoneNoneNoneNaNNaNNaNNoneNaNNaNNaNNaN
3592592895Wittmann, Peter13/2/195959----NaN2Serom thorakal bei Z.n. Spondylodiszitis Th7/8 b Z.n. Disztis mit epiduralem Empyem, Zn Solera Th6-7-9-10 + Deko+ Ausräumung BSF01011WKE TH8 + Teil WK710/8/201816/8/201814/8/2018STAU21None22.4NaN1101Z.n. Diszitiskein2001Fokussuche110.03.022.0xNaNNaNNaN
3602604561Gruenwald, Konrad22/6/193484----NaN2Spondylodiszitis BWK12/LWK1, Zn Deko L3/4/5110001. Perkutane CT-gesteuerte dorsale Stabilisierung (Solera) BWK11-12-LWK1-2NaNNaNNaNNaNNoneNoneNoneNaNNaNNaNNoneNoneNoneNoneNoneNoneNaNNaNNaNNoneNoneNoneNaNNaNNaNNoneNaNNaNNaNNaN
3631559145Meier, Franz17/7/1934811---NaN2Spondylodiszitis HW7/BW1, Zn ACDF HW4/501100ACDF+Platte HW7/BW118/4/201625/4/201628/4/2016Staphylococcus agalacticae21Staphylococcus agalacticae (BK, Knie)8.2NaN1101sVorOPKnie1111Fokussuche110.03.020.0xNaNNaNNaN
3642344995Duna, Georg12/12/193283---Adenom ColonNaN2Infektion LW4/5 und LW5/SW1 + Schraubenlockerung LWK4 b Z.n. Revisionsspondylodese 2015 bei Lockerung, Z.n. dorsoventraler Stabilisierung LW4-SW1 + Deko bei Stenose, Z.n. mehrfacher Deko LWS10001Revision mit Explantation der Schrauben LW4, Evakuation Pus BSF L4/5/S1, Viper L3-5-S221/3/201622/3/201623/3/2016Staphylococcus agalacticae21Staphylococcus agalacticae (BK, Knie)1.8NaN1101sVorOPEndokarditis2111Fokussuche180.02.020.0x1x herz SpectNaNNaN